diff --git "a/mlip_arena/tasks/diatomics/run.ipynb" "b/mlip_arena/tasks/diatomics/run.ipynb"
--- "a/mlip_arena/tasks/diatomics/run.ipynb"
+++ "b/mlip_arena/tasks/diatomics/run.ipynb"
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "3200850a-b8fb-4f50-9815-16ae8da0f942",
    "metadata": {
     "tags": []
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "90887faa-1601-4c4c-9c44-d16731471d7f",
    "metadata": {
     "scrolled": true,
@@ -41,24 +41,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== EquiformerV2(OC20) ==========\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.\n",
-      "WARNING:root:Couldn't modify the submission pickle with error: [Errno 2] No such file or directory: '/checkpoint/abhshkdz/open-catalyst-project/logs/equiformer_v2/8307793/30669435_submitted.pkl'\n",
-      "WARNING:root:Skipping scheduler setup. No training set found.\n",
-      "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/fairchem/core/modules/normalization/normalizer.py:69: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
-      "  \"mean\": torch.tensor(state_dict[\"mean\"]),\n"
+      "========== MACE-OFF(M) ==========\n",
+      "Selected GPU cuda:0 with 40338.06 MB free memory from 4 GPUs\n",
+      "Default dtype float32 does not match model dtype float64, converting models to float32.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb3a1b082e65432392452cd49c970a73",
+       "model_id": "60f14aee9df9484997239ace3de2e101",
        "version_major": 2,
        "version_minor": 0
       },
@@ -79,7 +70,7 @@
     {
      "data": {
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-       "model_id": "ae877fcff60b488b9bf999c8e60ede7b",
+       "model_id": "01f85ac837a44f64950df4fbe5f108f1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -100,7 +91,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3129493feb1341f8a481440ca48c3022",
+       "model_id": "ab613d7a045e45bd939214bfc6ff0b3f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -115,13 +106,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "2 is not in list\n",
       "Atoms(symbols='Li2', pbc=True, cell=[13.144000000000002, 13.145000000000001, 13.146000000000003])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "740fe86bac484a61b83a384823b1645b",
+       "model_id": "a44cf4d8d9af41ea98901956c5b2e800",
        "version_major": 2,
        "version_minor": 0
       },
@@ -136,13 +128,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "3 is not in list\n",
       "Atoms(symbols='Be2', pbc=True, cell=[12.276, 12.277, 12.278])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2649afe32f7486398ffd71c098b5481",
+       "model_id": "e3a16426d1cb4f9785fcfc1fd83dd8cd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -157,13 +150,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "4 is not in list\n",
       "Atoms(symbols='B2', pbc=True, cell=[11.842, 11.843, 11.844000000000001])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c7bbc608a47f413496fd445fb83dd747",
+       "model_id": "0b96202a2c594947b6093ecab0d89a25",
        "version_major": 2,
        "version_minor": 0
       },
@@ -178,13 +172,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "5 is not in list\n",
       "Atoms(symbols='C2', pbc=True, cell=[10.974, 10.975, 10.976])\n"
      ]
     },
     {
      "data": {
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        "version_minor": 0
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@@ -205,7 +200,7 @@
     {
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-       "model_id": "d26f8600f65f41a2a754ba489bd1fe64",
+       "model_id": "0c71c7705dbc428d81308cdc16942921",
        "version_major": 2,
        "version_minor": 0
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@@ -226,7 +221,7 @@
     {
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        "version_major": 2,
        "version_minor": 0
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@@ -247,7 +242,7 @@
     {
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-       "model_id": "87d7c2beadb94a98a85736521676a92f",
+       "model_id": "34a2485e76cd4acc8c5d4caa724037ae",
        "version_major": 2,
        "version_minor": 0
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@@ -268,7 +263,7 @@
     {
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-       "model_id": "c0b5603b4b7a4f798d28003cb8017e24",
+       "model_id": "da6bf461ce4849ef8b723a140aee5e46",
        "version_major": 2,
        "version_minor": 0
       },
@@ -283,13 +278,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "10 is not in list\n",
       "Atoms(symbols='Na2', pbc=True, cell=[15.5, 15.501, 15.502])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "717e21591149423f98c6ec8a57e2c007",
+       "model_id": "07b8b95e8418423193b3eb48a1c3197a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -304,13 +300,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "11 is not in list\n",
       "Atoms(symbols='Mg2', pbc=True, cell=[15.562, 15.562999999999999, 15.564])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c48f33f121084bc2a47c96a26bb24d40",
+       "model_id": "1032c8118d4e4da6b9ea753d152490b2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -325,13 +322,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "12 is not in list\n",
       "Atoms(symbols='Al2', pbc=True, cell=[13.950000000000001, 13.951, 13.952000000000002])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c49da4c1a00f4ef89ab53a8a58d8494c",
+       "model_id": "a907fe3e32644eaa884b9c11b80e503e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -346,13 +344,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "13 is not in list\n",
       "Atoms(symbols='Si2', pbc=True, cell=[13.578, 13.578999999999999, 13.58])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8062fddb9b7c4d5fbf7342785acc73bc",
+       "model_id": "fd88635852dc4d2ca1c3cd298a760a2e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -367,13 +366,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "14 is not in list\n",
       "Atoms(symbols='P2', pbc=True, cell=[11.78, 11.780999999999999, 11.782])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7d320bd7c5484dfab71ace8529b70d1d",
+       "model_id": "12bf703b946a4f038142fb8531dbdd44",
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        "version_minor": 0
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@@ -394,7 +394,7 @@
     {
      "data": {
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-       "model_id": "6324d2c298a542828c07291c18c221cb",
+       "model_id": "84232451800944dd931a9cc1a3f7c76a",
        "version_major": 2,
        "version_minor": 0
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@@ -415,7 +415,7 @@
     {
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-       "model_id": "54c5575c22574894b4818ffdf81a76d5",
+       "model_id": "49a6bc3910244f04b9a74e8d2e8c4142",
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@@ -436,7 +436,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "16ee4481621d4b2ab2814a926ec66961",
+       "model_id": "ebb843c27f634344bd10a526081ca13c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -451,13 +451,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "18 is not in list\n",
       "Atoms(symbols='K2', pbc=True, cell=[16.926000000000002, 16.927000000000003, 16.928])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "40c58d9009e14342b5d63daffa92e500",
+       "model_id": "c3cee6fcc4894e5085ce78549feb9177",
        "version_major": 2,
        "version_minor": 0
       },
@@ -472,13 +473,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "19 is not in list\n",
       "Atoms(symbols='Ca2', pbc=True, cell=[16.244, 16.245, 16.246])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6c26cc1757ea4f1c92a4260d2eebaca9",
+       "model_id": "df8ca2be9bfd496ca5cabda54ab5c6fc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -493,13 +495,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "20 is not in list\n",
       "Atoms(symbols='Sc2', pbc=True, cell=[15.996, 15.997, 15.998000000000001])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1a8a3149ff4f4c7797b366cab2419e19",
+       "model_id": "4861cf3841bf4bce95c6f28db390c90c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -514,13 +517,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "21 is not in list\n",
       "Atoms(symbols='Ti2', pbc=True, cell=[15.252, 15.253, 15.254000000000001])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e17caa1e90444469a529c782f74c6f44",
+       "model_id": "b8de0654f19c406da7709f59f4d5ab46",
        "version_major": 2,
        "version_minor": 0
       },
@@ -535,13 +539,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "22 is not in list\n",
       "Atoms(symbols='V2', pbc=True, cell=[15.004, 15.004999999999999, 15.006])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80181fe3673a4f9c81237113fb09241a",
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        "version_major": 2,
        "version_minor": 0
       },
@@ -556,13 +561,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "23 is not in list\n",
       "Atoms(symbols='Cr2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cec3efec611048898f3c253919e77c9c",
+       "model_id": "790a63d1478f4313b967ca5ac646b60e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -577,13 +583,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "24 is not in list\n",
       "Atoms(symbols='Mn2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0cbe0177d37145a09368da269439e9d3",
+       "model_id": "842404aaf77046fb95be4886c3c6b204",
        "version_major": 2,
        "version_minor": 0
       },
@@ -598,13 +605,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "25 is not in list\n",
       "Atoms(symbols='Fe2', pbc=True, cell=[15.128, 15.129, 15.13])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0224bdbfba704f7d8e7bbab53038785d",
+       "model_id": "421357cb56af4b589fdeafc354559700",
        "version_major": 2,
        "version_minor": 0
       },
@@ -619,13 +627,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "26 is not in list\n",
       "Atoms(symbols='Co2', pbc=True, cell=[14.879999999999999, 14.880999999999998, 14.882])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c9e09cee05b749e3b4be596a46d11584",
+       "model_id": "c0a0dc53185143dc99d99695bc93ffbc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -640,13 +649,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "27 is not in list\n",
       "Atoms(symbols='Ni2', pbc=True, cell=[14.879999999999999, 14.880999999999998, 14.882])\n"
      ]
     },
     {
      "data": {
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-       "model_id": "857608f7605140ecb55488b3ffbc0dc7",
+       "model_id": "686f870641b645e19ab34b492285d65b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -661,13 +671,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "28 is not in list\n",
       "Atoms(symbols='Cu2', pbc=True, cell=[14.756, 14.757, 14.758000000000001])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d5ed5324d4c401ca9479f06587a593b",
+       "model_id": "af3aa8ccc84a4e9c90cf7be3058f8974",
        "version_major": 2,
        "version_minor": 0
       },
@@ -682,13 +693,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "29 is not in list\n",
       "Atoms(symbols='Zn2', pbc=True, cell=[14.818000000000001, 14.819, 14.820000000000002])\n"
      ]
     },
     {
      "data": {
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        "version_major": 2,
        "version_minor": 0
       },
@@ -703,13 +715,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "30 is not in list\n",
       "Atoms(symbols='Ga2', pbc=True, cell=[14.383999999999999, 14.384999999999998, 14.386])\n"
      ]
     },
     {
      "data": {
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        "version_major": 2,
        "version_minor": 0
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@@ -724,13 +737,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "31 is not in list\n",
       "Atoms(symbols='Ge2', pbc=True, cell=[14.198, 14.199, 14.200000000000001])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "169860b84069488380ae557fb7cf5ed1",
+       "model_id": "3f974765427c4cb5ac7eea72a57cfa58",
        "version_major": 2,
        "version_minor": 0
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@@ -745,13 +759,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "32 is not in list\n",
       "Atoms(symbols='As2', pbc=True, cell=[11.655999999999999, 11.656999999999998, 11.658])\n"
      ]
     },
     {
      "data": {
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-       "model_id": "0a76df7eccb14dea84019b81b0fd0b3e",
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        "version_major": 2,
        "version_minor": 0
       },
@@ -766,13 +781,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "33 is not in list\n",
       "Atoms(symbols='Se2', pbc=True, cell=[11.284, 11.285, 11.286000000000001])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a7187637bd0449baa9ba01567467692f",
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        "version_major": 2,
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@@ -787,13 +803,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "34 is not in list\n",
       "Atoms(symbols='Br2', pbc=True, cell=[11.532000000000002, 11.533000000000001, 11.534000000000002])\n"
      ]
     },
     {
      "data": {
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-       "model_id": "08eecc5ce1da4e4a852a5439bc2d7845",
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@@ -814,7 +831,7 @@
     {
      "data": {
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-       "model_id": "bd3e4d3b58e14a9783ee394a15e1aca1",
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        "version_major": 2,
        "version_minor": 0
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@@ -829,13 +846,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "36 is not in list\n",
       "Atoms(symbols='Rb2', pbc=True, cell=[19.902, 19.903000000000002, 19.904])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d304b8fb931645d8b7f0ff5e31fcb21f",
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        "version_major": 2,
        "version_minor": 0
       },
@@ -850,13 +868,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "37 is not in list\n",
       "Atoms(symbols='Sr2', pbc=True, cell=[17.608, 17.609, 17.61])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "48b5a4ab0c6c46eea0623f3b419935ed",
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        "version_major": 2,
        "version_minor": 0
       },
@@ -871,13 +890,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "38 is not in list\n",
       "Atoms(symbols='Y2', pbc=True, cell=[17.05, 17.051000000000002, 17.052])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5e95b61650f6494fbd300072e7f8ad60",
+       "model_id": "f83629d595504b658a703122591784a1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -892,13 +912,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "39 is not in list\n",
       "Atoms(symbols='Zr2', pbc=True, cell=[15.624, 15.625, 15.626000000000001])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1abe8f3089954547af62d929e97d55fa",
+       "model_id": "31f0e507a3414cde8f21821b16eae5ca",
        "version_major": 2,
        "version_minor": 0
       },
@@ -913,13 +934,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "40 is not in list\n",
       "Atoms(symbols='Nb2', pbc=True, cell=[15.872000000000002, 15.873000000000001, 15.874000000000002])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "94f02034d0cd465181de8b39084dad42",
+       "model_id": "25d1d0e58c5948b7af0bf6435f32a5af",
        "version_major": 2,
        "version_minor": 0
       },
@@ -934,13 +956,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "41 is not in list\n",
       "Atoms(symbols='Mo2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2210fd8038384aa6888b45285c474e47",
+       "model_id": "f0fa99be9cb04af081dcb4ce3d7728f2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -950,321 +973,265 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for model in MLIPEnum:\n",
-    "    \n",
-    "    model_name = model.name\n",
-    "    \n",
-    "    if model_name == 'EquiformerV2(OC22)':\n",
-    "        continue\n",
-    "    \n",
-    "    json_fpath = Path(REGISTRY[model_name][\"family\"]) / \"homonuclear-diatomics.json\"\n",
-    "    \n",
-    "    if json_fpath.exists():\n",
-    "        continue\n",
-    "    \n",
-    "    print(f\"========== {model_name} ==========\")\n",
-    "\n",
-    "    calc = MLIPEnum[model_name].value()\n",
-    "\n",
-    "    for symbol in tqdm(chemical_symbols[1:]):\n",
-    "\n",
-    "        s = set([symbol])\n",
-    "\n",
-    "        if \"X\" in s:\n",
-    "            continue\n",
-    "\n",
-    "        try:\n",
-    "            atom = Atom(symbol)\n",
-    "            rmin = 0.9 * covalent_radii[atom.number]\n",
-    "            rvdw = vdw_alvarez.vdw_radii[atom.number] if atom.number < len(vdw_alvarez.vdw_radii) else np.nan\n",
-    "            rmax = 3.1 * rvdw if not np.isnan(rvdw) else 6\n",
-    "            rstep = 0.01\n",
-    "\n",
-    "            a = 2 * rmax\n",
-    "\n",
-    "            npts = int((rmax - rmin)/rstep)\n",
-    "\n",
-    "            rs = np.linspace(rmin, rmax, npts)\n",
-    "            es = np.zeros_like(rs)\n",
-    "\n",
-    "            da = symbol + symbol\n",
-    "\n",
-    "            out_dir = Path(REGISTRY[model_name][\"family\"]) / str(da)\n",
-    "            os.makedirs(out_dir, exist_ok=True)\n",
-    "\n",
-    "            skip = 0\n",
-    "\n",
-    "            element = Element(symbol)\n",
-    "\n",
-    "            try:\n",
-    "                m = element.valence[1]\n",
-    "                if element.valence == (0, 2):\n",
-    "                    m = 0\n",
-    "            except:\n",
-    "                m = 0\n",
-    "\n",
-    "\n",
-    "            r = rs[0]\n",
-    "\n",
-    "            positions = [\n",
-    "                [a/2-r/2, a/2, a/2],\n",
-    "                [a/2+r/2, a/2, a/2],\n",
-    "            ]\n",
-    "\n",
-    "            traj_fpath = out_dir / f\"{model_name}.extxyz\"\n",
-    "\n",
-    "            if traj_fpath.exists():\n",
-    "                traj = read(traj_fpath, index=\":\")\n",
-    "                skip = len(traj)\n",
-    "                atoms = traj[-1]\n",
-    "            else:\n",
-    "                # Create the unit cell with two atoms\n",
-    "                atoms = Atoms(\n",
-    "                    da,\n",
-    "                    positions=positions,\n",
-    "                    # magmoms=magmoms,\n",
-    "                    cell=[a, a+0.001, a+0.002],\n",
-    "                    pbc=True\n",
-    "                )\n",
-    "\n",
-    "            print(atoms)\n",
-    "\n",
-    "            atoms.calc = calc\n",
-    "\n",
-    "            for i, r in enumerate(tqdm(rs)):\n",
-    "\n",
-    "                if i < skip:\n",
-    "                    continue\n",
-    "\n",
-    "                positions = [\n",
-    "                    [a/2-r/2, a/2, a/2],\n",
-    "                    [a/2+r/2, a/2, a/2],\n",
-    "                ]\n",
-    "\n",
-    "                # atoms.set_initial_magnetic_moments(magmoms)\n",
-    "\n",
-    "                atoms.set_positions(positions)\n",
-    "\n",
-    "                es[i] = atoms.get_potential_energy()\n",
-    "\n",
-    "                write(traj_fpath, atoms, append=\"a\")\n",
-    "        except Exception as e:\n",
-    "            print(e)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a0ac2c09-370b-4fdd-bf74-ea5c4ade0215",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== MACE-MP(M) ==========\n"
+      "42 is not in list\n",
+      "Atoms(symbols='Tc2', pbc=True, cell=[15.128, 15.129, 15.13])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "597310b16837405fa6674583f7ea6892",
+       "model_id": "937365c9a05a4cabb90e4f56bf63ad51",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/624 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
-      "/tmp/ipykernel_768993/102531167.py:101: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
-      "  \"spearman-descending-force\": stats.spearmanr(rs[iminf:], fs[iminf:]).statistic,\n",
-      "/tmp/ipykernel_768993/102531167.py:103: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
-      "  \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== CHGNet ==========\n"
+      "43 is not in list\n",
+      "Atoms(symbols='Ru2', pbc=True, cell=[15.252, 15.253, 15.254000000000001])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7e33d5e27fc144dfa24b28307d003e18",
+       "model_id": "0aa4efe49e48493c974959853942d76b",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/631 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n"
+      "44 is not in list\n",
+      "Atoms(symbols='Rh2', pbc=True, cell=[15.128, 15.129, 15.13])\n"
      ]
     },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dec48a374ed7401d91cbe262d527e39c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/628 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== M3GNet ==========\n"
+      "45 is not in list\n",
+      "Atoms(symbols='Pd2', pbc=True, cell=[13.33, 13.331, 13.332])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9e2521f6b5314c65adadb27928af771b",
+       "model_id": "85b8216dcd1b4178b56d9ae347782b99",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/541 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
-      "/tmp/ipykernel_768993/102531167.py:102: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
-      "  \"spearman-ascending-force\": stats.spearmanr(rs[:iminf], fs[:iminf]).statistic,\n"
+      "46 is not in list\n",
+      "Atoms(symbols='Ag2', pbc=True, cell=[15.686, 15.687, 15.688])\n"
      ]
     },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb01328f2d7e4e2cae40113d6d5bd9d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/653 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== ORB ==========\n"
+      "47 is not in list\n",
+      "Atoms(symbols='Cd2', pbc=True, cell=[15.438000000000002, 15.439000000000002, 15.440000000000003])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dc4eb14a89484a89a0f3efceddb1101a",
+       "model_id": "3d01ff97a4d143c687c9bdb1c3a9c849",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/642 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n"
+      "48 is not in list\n",
+      "Atoms(symbols='In2', pbc=True, cell=[15.066, 15.067, 15.068000000000001])\n"
      ]
     },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a5d8d48ed684b9b94d18146443f6b8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/625 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== SevenNet ==========\n"
+      "49 is not in list\n",
+      "Atoms(symbols='Sn2', pbc=True, cell=[15.004, 15.004999999999999, 15.006])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0701d73663f5483ba64f000208e73b97",
+       "model_id": "f42b59ee5d74427088ed03dda2be8987",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/625 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
-      "/tmp/ipykernel_768993/102531167.py:104: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
-      "  \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n"
+      "50 is not in list\n",
+      "Atoms(symbols='Sb2', pbc=True, cell=[15.314000000000002, 15.315000000000001, 15.316000000000003])\n"
      ]
     },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "720434d9f6fe4cbaada16a5bb1ec6450",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/640 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== EquiformerV2(OC22) ==========\n"
+      "51 is not in list\n",
+      "Atoms(symbols='Te2', pbc=True, cell=[12.338000000000001, 12.339, 12.340000000000002])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b773e96037947d9b51f710e50b3635b",
+       "model_id": "53726bf903e545bc82afe94eb0cf233c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/492 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
-      "/tmp/ipykernel_768993/102531167.py:103: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
-      "  \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n",
-      "/tmp/ipykernel_768993/102531167.py:104: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
-      "  \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n"
+      "52 is not in list\n",
+      "Atoms(symbols='I2', pbc=True, cell=[12.648000000000001, 12.649000000000001, 12.650000000000002])\n"
      ]
     },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc018829104e4b719a12870c7ddfc89e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/507 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== EquiformerV2(OC20) ==========\n"
+      "Atoms(symbols='Xe2', pbc=True, cell=[12.772, 12.773, 12.774000000000001])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f5f6c8438a9c4a3892d6b3cbf94f1910",
+       "model_id": "9cece0b309864ddb83616ae5447e1971",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/512 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1274,26 +1241,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== eSCN(OC20) ==========\n"
+      "54 is not in list\n",
+      "Atoms(symbols='Cs2', pbc=True, cell=[21.576, 21.577, 21.578])\n"
      ]
     },
     {
-     "name": "stderr",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb4596c6422b4318a71b912aeca8e47d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/859 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_768993/102531167.py:114: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  df = pd.concat([df0, df], ignore_index=True)\n"
+      "55 is not in list\n",
+      "Atoms(symbols='Ba2', pbc=True, cell=[18.785999999999998, 18.787, 18.787999999999997])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4f3e200efba948aea10d1d937cd787fd",
+       "model_id": "54e7da5565bf45459b919b1084bb1032",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/745 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1303,18 +1285,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== MACE-OFF(M) ==========\n"
+      "56 is not in list\n",
+      "Atoms(symbols='La2', pbc=True, cell=[18.476, 18.477, 18.477999999999998])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e51884511ccc43338c5e621b7b6c35be",
+       "model_id": "9cf99aa57c9144729661e4ed75a49034",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/737 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1324,8371 +1307,2356 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "========== ALIGNN ==========\n"
+      "57 is not in list\n",
+      "Atoms(symbols='Ce2', pbc=True, cell=[17.855999999999998, 17.857, 17.857999999999997])\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "85868bdd44274852b7e8f414c36e2a8a",
+       "model_id": "892378b456c349daa06ce702064620bd",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/118 [00:00<?, ?it/s]"
+       "  0%|          | 0/709 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
-     "ename": "OSError",
-     "evalue": "Cannot save file into a non-existent directory: 'alignn'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[3], line 117\u001b[0m\n\u001b[1;32m    114\u001b[0m     df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([df0, df], ignore_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m    115\u001b[0m     df\u001b[38;5;241m.\u001b[39mdrop_duplicates(inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, subset\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmethod\u001b[39m\u001b[38;5;124m\"\u001b[39m], keep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlast\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 117\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_fpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrecords\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/util/_decorators.py:333\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    328\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    329\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    330\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    331\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    332\u001b[0m     )\n\u001b[0;32m--> 333\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/core/generic.py:2702\u001b[0m, in \u001b[0;36mNDFrame.to_json\u001b[0;34m(self, path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options, mode)\u001b[0m\n\u001b[1;32m   2699\u001b[0m config\u001b[38;5;241m.\u001b[39mis_nonnegative_int(indent)\n\u001b[1;32m   2700\u001b[0m indent \u001b[38;5;241m=\u001b[39m indent \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m-> 2702\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_json\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2703\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2704\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2705\u001b[0m \u001b[43m    \u001b[49m\u001b[43morient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2706\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdate_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2707\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdouble_precision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdouble_precision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2708\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_ascii\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_ascii\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2709\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdate_unit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_unit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2710\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdefault_handler\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_handler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2711\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlines\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2712\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2713\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2715\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2717\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/io/json/_json.py:217\u001b[0m, in \u001b[0;36mto_json\u001b[0;34m(path_or_buf, obj, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options, mode)\u001b[0m\n\u001b[1;32m    213\u001b[0m     s \u001b[38;5;241m=\u001b[39m convert_to_line_delimits(s)\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m path_or_buf \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# apply compression and byte/text conversion\u001b[39;00m\n\u001b[0;32m--> 217\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    218\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\n\u001b[1;32m    219\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m    220\u001b[0m         handles\u001b[38;5;241m.\u001b[39mhandle\u001b[38;5;241m.\u001b[39mwrite(s)\n\u001b[1;32m    221\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/io/common.py:749\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    747\u001b[0m \u001b[38;5;66;03m# Only for write methods\u001b[39;00m\n\u001b[1;32m    748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode \u001b[38;5;129;01mand\u001b[39;00m is_path:\n\u001b[0;32m--> 749\u001b[0m     \u001b[43mcheck_parent_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression:\n\u001b[1;32m    752\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzstd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    753\u001b[0m         \u001b[38;5;66;03m# compression libraries do not like an explicit text-mode\u001b[39;00m\n",
-      "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/io/common.py:616\u001b[0m, in \u001b[0;36mcheck_parent_directory\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m    614\u001b[0m parent \u001b[38;5;241m=\u001b[39m Path(path)\u001b[38;5;241m.\u001b[39mparent\n\u001b[1;32m    615\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_dir():\n\u001b[0;32m--> 616\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot save file into a non-existent directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[0;31mOSError\u001b[0m: Cannot save file into a non-existent directory: 'alignn'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "58 is not in list\n",
+      "Atoms(symbols='Pr2', pbc=True, cell=[18.104, 18.105, 18.105999999999998])\n"
      ]
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "for model in MLIPEnum:\n",
-    "    \n",
-    "    model_name = model.name\n",
-    "    \n",
-    "    print(f\"========== {model_name} ==========\")\n",
-    "    \n",
-    "    df = pd.DataFrame(columns=[\n",
-    "        \"name\", \n",
-    "        \"method\", \n",
-    "        \"R\", \"E\", \"F\", \"S^2\", \n",
-    "        \"force-flip-times\",\n",
-    "        \"force-total-variation\",\n",
-    "        \"energy-diff-flip-times\",\n",
-    "        \"energy-grad-norm-max\",\n",
-    "        \"energy-jump\",\n",
-    "        \"energy-total-variation\",\n",
-    "        \"conservation-deviation\",\n",
-    "        \"spearman-descending-force\",\n",
-    "        \"spearman-ascending-force\",\n",
-    "        \"spearman-repulsion-energy\",\n",
-    "        \"spearman-attraction-energy\"\n",
-    "    ])\n",
-    "    \n",
-    "\n",
-    "    for symbol in tqdm(chemical_symbols[1:]):\n",
-    "\n",
-    "        da = symbol + symbol\n",
-    "\n",
-    "        out_dir = Path(REGISTRY[model_name][\"family\"]) / da\n",
-    "\n",
-    "        traj_fpath = out_dir / f\"{model_name}.extxyz\"\n",
-    "\n",
-    "\n",
-    "        if traj_fpath.exists():\n",
-    "            traj = read(traj_fpath, index=\":\")\n",
-    "        else:\n",
-    "            continue\n",
-    "\n",
-    "        Rs, Es, Fs, S2s = [], [], [], []\n",
-    "        for atoms in traj:\n",
-    "\n",
-    "            vec = atoms.positions[1] - atoms.positions[0]\n",
-    "            r = np.linalg.norm(vec)\n",
-    "            e = atoms.get_potential_energy()\n",
-    "            f = np.inner(vec/r, atoms.get_forces()[1])\n",
-    "            # s2 = np.mean(np.power(atoms.get_magnetic_moments(), 2))\n",
-    "\n",
-    "            Rs.append(r)\n",
-    "            Es.append(e)\n",
-    "            Fs.append(f)\n",
-    "            # S2s.append(s2)\n",
-    "\n",
-    "        rs = np.array(Rs)\n",
-    "        es = np.array(Es)\n",
-    "        fs = np.array(Fs)\n",
-    "\n",
-    "        indices = np.argsort(rs)[::-1]\n",
-    "        rs = rs[indices]\n",
-    "        es = es[indices]\n",
-    "        fs = fs[indices]\n",
-    "\n",
-    "        iminf = np.argmin(fs)\n",
-    "        imine = np.argmin(es)\n",
-    "\n",
-    "        de_dr = np.gradient(es, rs)\n",
-    "        d2e_dr2 = np.gradient(de_dr, rs)\n",
-    "\n",
-    "        rounded_fs = np.copy(fs)\n",
-    "        rounded_fs[np.abs(rounded_fs) < 1e-2] = 0\n",
-    "        fs_sign = np.sign(rounded_fs)\n",
-    "        fs_sign = fs_sign[fs_sign != 0]\n",
-    "\n",
-    "        # rounded_ediff = np.diff(es)\n",
-    "        # rounded_ediff[np.abs(rounded_ediff) < zero_threshold] = 0\n",
-    "        ediff = np.diff(es)\n",
-    "        ediff[np.abs(ediff) < 1e-3] = 0\n",
-    "        ediff_sign = np.sign(ediff)\n",
-    "        mask = ediff_sign != 0\n",
-    "        ediff = ediff[mask]\n",
-    "        ediff_sign = ediff_sign[mask]\n",
-    "        ediff_flip = np.diff(ediff_sign) != 0\n",
-    "        ejump = np.abs(ediff[:-1][ediff_flip]).sum() + np.abs(ediff[1:][ediff_flip]).sum()\n",
-    "\n",
-    "        conservation_deviation = np.mean(np.abs(fs + de_dr))\n",
-    "\n",
-    "        data = {\n",
-    "            \"name\": da,\n",
-    "            \"method\": model_name,\n",
-    "            \"R\": rs,\n",
-    "            \"E\": es,\n",
-    "            \"F\": fs,\n",
-    "            \"S^2\": S2s,\n",
-    "            \"force-flip-times\": np.sum(np.diff(fs_sign)!=0),\n",
-    "            \"force-total-variation\": np.sum(np.abs(np.diff(fs))),\n",
-    "            \"energy-diff-flip-times\": np.sum(ediff_flip),\n",
-    "            \"energy-grad-norm-max\": np.max(np.abs(de_dr)),\n",
-    "            \"energy-jump\": ejump,\n",
-    "            # \"energy-grad-norm-mean\": np.mean(de_dr_abs),\n",
-    "            \"energy-total-variation\": np.sum(np.abs(np.diff(es))),\n",
-    "            \"conservation-deviation\": conservation_deviation,\n",
-    "            \"spearman-descending-force\": stats.spearmanr(rs[iminf:], fs[iminf:]).statistic,\n",
-    "            \"spearman-ascending-force\": stats.spearmanr(rs[:iminf], fs[:iminf]).statistic,\n",
-    "            \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n",
-    "            \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n",
-    "        }\n",
-    "\n",
-    "        df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
-    "\n",
-    "\n",
-    "    json_fpath = Path(REGISTRY[model_name][\"family\"]) / \"homonuclear-diatomics.json\"\n",
-    "\n",
-    "    if json_fpath.exists():\n",
-    "        df0 = pd.read_json(json_fpath)\n",
-    "        df = pd.concat([df0, df], ignore_index=True)\n",
-    "        df.drop_duplicates(inplace=True, subset=[\"name\", \"method\"], keep='last')\n",
-    "\n",
-    "    df.to_json(json_fpath, orient=\"records\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "e0dd4367-3dca-440f-a7a9-7fdd84183f2c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
      "data": {
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>method</th>\n",
-       "      <th>R</th>\n",
-       "      <th>E</th>\n",
-       "      <th>F</th>\n",
-       "      <th>S^2</th>\n",
-       "      <th>force-flip-times</th>\n",
-       "      <th>force-total-variation</th>\n",
-       "      <th>energy-diff-flip-times</th>\n",
-       "      <th>energy-grad-norm-max</th>\n",
-       "      <th>energy-jump</th>\n",
-       "      <th>energy-total-variation</th>\n",
-       "      <th>conservation-deviation</th>\n",
-       "      <th>spearman-descending-force</th>\n",
-       "      <th>spearman-ascending-force</th>\n",
-       "      <th>spearman-repulsion-energy</th>\n",
-       "      <th>spearman-attraction-energy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>HH</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[3.7199999999999998, 3.70996794, 3.69993586, 3...</td>\n",
-       "      <td>[-2.348365306854248, -2.3483591079711914, -2.3...</td>\n",
-       "      <td>[1.7e-07, 0.00035892, 0.00068535, 0.00112034, ...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>3</td>\n",
-       "      <td>80.309463</td>\n",
-       "      <td>2</td>\n",
-       "      <td>65.875268</td>\n",
-       "      <td>0.009835</td>\n",
-       "      <td>15.816339</td>\n",
-       "      <td>0.043480</td>\n",
-       "      <td>-0.999421</td>\n",
-       "      <td>0.840215</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.794565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>HeHe</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[4.433, 4.4229736200000005, 4.41294724, 4.4029...</td>\n",
-       "      <td>[0.022882699966430664, 0.022882461547851562, 0...</td>\n",
-       "      <td>[0.0, -0.00010788, -0.00021458, -0.00032269, -...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.485210</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.821236</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.344755</td>\n",
-       "      <td>0.001872</td>\n",
-       "      <td>-0.904433</td>\n",
-       "      <td>0.077295</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.093575</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>LiLi</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[6.572000000000001, 6.561981520000001, 6.55196...</td>\n",
-       "      <td>[-1.8418288230895996, -1.8418288230895996, -1....</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8.583572</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8.122311</td>\n",
-       "      <td>0.002224</td>\n",
-       "      <td>5.332280</td>\n",
-       "      <td>0.013703</td>\n",
-       "      <td>-0.999858</td>\n",
-       "      <td>0.993021</td>\n",
-       "      <td>-0.999996</td>\n",
-       "      <td>0.995153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>BeBe</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[6.138000000000001, 6.12797338, 6.117946759999...</td>\n",
-       "      <td>[-0.6769685745239258, -0.6769685745239258, -0....</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>29.133944</td>\n",
-       "      <td>2</td>\n",
-       "      <td>10.346194</td>\n",
-       "      <td>0.016283</td>\n",
-       "      <td>6.239225</td>\n",
-       "      <td>0.022079</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-0.044580</td>\n",
-       "      <td>-0.998691</td>\n",
-       "      <td>0.999892</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>BB</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[5.921000000000001, 5.91097088, 5.900941739999...</td>\n",
-       "      <td>[-1.404820442199707, -1.404820442199707, -1.40...</td>\n",
-       "      <td>[-6e-08, -0.00011319, -0.00023184, -0.0003534,...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>330.078666</td>\n",
-       "      <td>2</td>\n",
-       "      <td>123.581618</td>\n",
-       "      <td>0.543755</td>\n",
-       "      <td>40.039458</td>\n",
-       "      <td>0.138081</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.201590</td>\n",
-       "      <td>-0.999921</td>\n",
-       "      <td>0.999947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>173</th>\n",
-       "      <td>ThTh</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[9.082999999999998, 9.07297364, 9.062947300000...</td>\n",
-       "      <td>[-4.095590591430664, -4.095590591430664, -4.09...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>16.226657</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6.413536</td>\n",
-       "      <td>0.006823</td>\n",
-       "      <td>3.392294</td>\n",
-       "      <td>0.024297</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.026348</td>\n",
-       "      <td>-0.997169</td>\n",
-       "      <td>0.918191</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>PaPa</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[8.927999999999999, 8.91797468, 8.90794936, 8....</td>\n",
-       "      <td>[-8.707170486450195, -8.707170486450195, -8.70...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6.407403</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.536645</td>\n",
-       "      <td>0.006878</td>\n",
-       "      <td>2.349456</td>\n",
-       "      <td>0.018977</td>\n",
-       "      <td>-0.627256</td>\n",
-       "      <td>0.460505</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.950999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>175</th>\n",
-       "      <td>UU</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[8.401, 8.390974320000002, 8.38094864, 8.37092...</td>\n",
-       "      <td>[-11.437246322631836, -11.437246322631836, -11...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>34.878494</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25.824510</td>\n",
-       "      <td>0.004967</td>\n",
-       "      <td>11.734995</td>\n",
-       "      <td>0.025810</td>\n",
-       "      <td>-0.974165</td>\n",
-       "      <td>0.939272</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.966465</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>176</th>\n",
-       "      <td>NpNp</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[8.741999999999999, 8.7319829, 8.72196582, 8.7...</td>\n",
-       "      <td>[-18.34619903564453, -18.34619903564453, -18.3...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>3</td>\n",
-       "      <td>14.386345</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8.632403</td>\n",
-       "      <td>0.010139</td>\n",
-       "      <td>2.824085</td>\n",
-       "      <td>0.015532</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.189479</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.439673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>177</th>\n",
-       "      <td>PuPu</td>\n",
-       "      <td>MACE-MP(M)</td>\n",
-       "      <td>[8.710999999999999, 8.70097432, 8.690948639999...</td>\n",
-       "      <td>[-23.70560073852539, -23.70560073852539, -23.7...</td>\n",
-       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>10.916421</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6.815384</td>\n",
-       "      <td>0.005814</td>\n",
-       "      <td>3.295753</td>\n",
-       "      <td>0.012909</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>0.115110</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.260405</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>89 rows × 17 columns</p>\n",
-       "</div>"
-      ],
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       "text/plain": [
-       "     name      method                                                  R  \\\n",
-       "89     HH  MACE-MP(M)  [3.7199999999999998, 3.70996794, 3.69993586, 3...   \n",
-       "90   HeHe  MACE-MP(M)  [4.433, 4.4229736200000005, 4.41294724, 4.4029...   \n",
-       "91   LiLi  MACE-MP(M)  [6.572000000000001, 6.561981520000001, 6.55196...   \n",
-       "92   BeBe  MACE-MP(M)  [6.138000000000001, 6.12797338, 6.117946759999...   \n",
-       "93     BB  MACE-MP(M)  [5.921000000000001, 5.91097088, 5.900941739999...   \n",
-       "..    ...         ...                                                ...   \n",
-       "173  ThTh  MACE-MP(M)  [9.082999999999998, 9.07297364, 9.062947300000...   \n",
-       "174  PaPa  MACE-MP(M)  [8.927999999999999, 8.91797468, 8.90794936, 8....   \n",
-       "175    UU  MACE-MP(M)  [8.401, 8.390974320000002, 8.38094864, 8.37092...   \n",
-       "176  NpNp  MACE-MP(M)  [8.741999999999999, 8.7319829, 8.72196582, 8.7...   \n",
-       "177  PuPu  MACE-MP(M)  [8.710999999999999, 8.70097432, 8.690948639999...   \n",
-       "\n",
-       "                                                     E  \\\n",
-       "89   [-2.348365306854248, -2.3483591079711914, -2.3...   \n",
-       "90   [0.022882699966430664, 0.022882461547851562, 0...   \n",
-       "91   [-1.8418288230895996, -1.8418288230895996, -1....   \n",
-       "92   [-0.6769685745239258, -0.6769685745239258, -0....   \n",
-       "93   [-1.404820442199707, -1.404820442199707, -1.40...   \n",
-       "..                                                 ...   \n",
-       "173  [-4.095590591430664, -4.095590591430664, -4.09...   \n",
-       "174  [-8.707170486450195, -8.707170486450195, -8.70...   \n",
-       "175  [-11.437246322631836, -11.437246322631836, -11...   \n",
-       "176  [-18.34619903564453, -18.34619903564453, -18.3...   \n",
-       "177  [-23.70560073852539, -23.70560073852539, -23.7...   \n",
-       "\n",
-       "                                                     F S^2 force-flip-times  \\\n",
-       "89   [1.7e-07, 0.00035892, 0.00068535, 0.00112034, ...  []                3   \n",
-       "90   [0.0, -0.00010788, -0.00021458, -0.00032269, -...  []                2   \n",
-       "91   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  []                1   \n",
-       "92   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  []                2   \n",
-       "93   [-6e-08, -0.00011319, -0.00023184, -0.0003534,...  []                2   \n",
-       "..                                                 ...  ..              ...   \n",
-       "173  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  []                2   \n",
-       "174  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  []                2   \n",
-       "175  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  []                1   \n",
-       "176  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  []                3   \n",
-       "177  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  []                2   \n",
-       "\n",
-       "     force-total-variation energy-diff-flip-times  energy-grad-norm-max  \\\n",
-       "89               80.309463                      2             65.875268   \n",
-       "90                1.485210                      0              0.821236   \n",
-       "91                8.583572                      1              8.122311   \n",
-       "92               29.133944                      2             10.346194   \n",
-       "93              330.078666                      2            123.581618   \n",
-       "..                     ...                    ...                   ...   \n",
-       "173              16.226657                      2              6.413536   \n",
-       "174               6.407403                      2              2.536645   \n",
-       "175              34.878494                      1             25.824510   \n",
-       "176              14.386345                      3              8.632403   \n",
-       "177              10.916421                      2              6.815384   \n",
-       "\n",
-       "     energy-jump  energy-total-variation  conservation-deviation  \\\n",
-       "89      0.009835               15.816339                0.043480   \n",
-       "90      0.000000                0.344755                0.001872   \n",
-       "91      0.002224                5.332280                0.013703   \n",
-       "92      0.016283                6.239225                0.022079   \n",
-       "93      0.543755               40.039458                0.138081   \n",
-       "..           ...                     ...                     ...   \n",
-       "173     0.006823                3.392294                0.024297   \n",
-       "174     0.006878                2.349456                0.018977   \n",
-       "175     0.004967               11.734995                0.025810   \n",
-       "176     0.010139                2.824085                0.015532   \n",
-       "177     0.005814                3.295753                0.012909   \n",
-       "\n",
-       "     spearman-descending-force  spearman-ascending-force  \\\n",
-       "89                   -0.999421                  0.840215   \n",
-       "90                   -0.904433                  0.077295   \n",
-       "91                   -0.999858                  0.993021   \n",
-       "92                         NaN                 -0.044580   \n",
-       "93                         NaN                  0.201590   \n",
-       "..                         ...                       ...   \n",
-       "173                        NaN                  0.026348   \n",
-       "174                  -0.627256                  0.460505   \n",
-       "175                  -0.974165                  0.939272   \n",
-       "176                  -1.000000                  0.189479   \n",
-       "177                  -1.000000                  0.115110   \n",
-       "\n",
-       "     spearman-repulsion-energy  spearman-attraction-energy  \n",
-       "89                   -1.000000                    0.794565  \n",
-       "90                   -1.000000                    0.093575  \n",
-       "91                   -0.999996                    0.995153  \n",
-       "92                   -0.998691                    0.999892  \n",
-       "93                   -0.999921                    0.999947  \n",
-       "..                         ...                         ...  \n",
-       "173                  -0.997169                    0.918191  \n",
-       "174                        NaN                    0.950999  \n",
-       "175                  -1.000000                    0.966465  \n",
-       "176                  -1.000000                    0.439673  \n",
-       "177                        NaN                    0.260405  \n",
-       "\n",
-       "[89 rows x 17 columns]"
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "116 is not in list\n",
+      "Atoms(symbols='Ts2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
+     ]
+    },
+    {
+     "data": {
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-      "model_name": "LayoutModel",
-      "state": {}
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "117 is not in list\n",
+      "Atoms(symbols='Og2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
+     ]
+    },
+    {
+     "data": {
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-       "layout": "IPY_MODEL_4bd08eca8a1747469412bd6df4acc1b8"
-      }
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "118 is not in list\n"
+     ]
+    }
+   ],
+   "source": [
+    "for model in MLIPEnum:\n",
+    "    \n",
+    "    model_name = model.name\n",
+    "    \n",
+    "    json_fpath = Path(REGISTRY[model_name][\"family\"]) / \"homonuclear-diatomics.json\"\n",
+    "    \n",
+    "    if json_fpath.exists():\n",
+    "        continue\n",
+    "    \n",
+    "    print(f\"========== {model_name} ==========\")\n",
+    "\n",
+    "    calc = MLIPEnum[model_name].value()\n",
+    "\n",
+    "    for symbol in tqdm(chemical_symbols[1:]):\n",
+    "\n",
+    "        s = set([symbol])\n",
+    "\n",
+    "        if \"X\" in s:\n",
+    "            continue\n",
+    "\n",
+    "        try:\n",
+    "            atom = Atom(symbol)\n",
+    "            rmin = 0.9 * covalent_radii[atom.number]\n",
+    "            rvdw = vdw_alvarez.vdw_radii[atom.number] if atom.number < len(vdw_alvarez.vdw_radii) else np.nan\n",
+    "            rmax = 3.1 * rvdw if not np.isnan(rvdw) else 6\n",
+    "            rstep = 0.01\n",
+    "\n",
+    "            a = 2 * rmax\n",
+    "\n",
+    "            npts = int((rmax - rmin)/rstep)\n",
+    "\n",
+    "            rs = np.linspace(rmin, rmax, npts)\n",
+    "            es = np.zeros_like(rs)\n",
+    "\n",
+    "            da = symbol + symbol\n",
+    "\n",
+    "            out_dir = Path(REGISTRY[model_name][\"family\"]) / str(da)\n",
+    "            os.makedirs(out_dir, exist_ok=True)\n",
+    "\n",
+    "            skip = 0\n",
+    "\n",
+    "            element = Element(symbol)\n",
+    "\n",
+    "            try:\n",
+    "                m = element.valence[1]\n",
+    "                if element.valence == (0, 2):\n",
+    "                    m = 0\n",
+    "            except:\n",
+    "                m = 0\n",
+    "\n",
+    "\n",
+    "            r = rs[0]\n",
+    "\n",
+    "            positions = [\n",
+    "                [a/2-r/2, a/2, a/2],\n",
+    "                [a/2+r/2, a/2, a/2],\n",
+    "            ]\n",
+    "\n",
+    "            traj_fpath = out_dir / f\"{model_name}.extxyz\"\n",
+    "\n",
+    "            if traj_fpath.exists():\n",
+    "                traj = read(traj_fpath, index=\":\")\n",
+    "                skip = len(traj)\n",
+    "                atoms = traj[-1]\n",
+    "            else:\n",
+    "                # Create the unit cell with two atoms\n",
+    "                atoms = Atoms(\n",
+    "                    da,\n",
+    "                    positions=positions,\n",
+    "                    # magmoms=magmoms,\n",
+    "                    cell=[a, a+0.001, a+0.002],\n",
+    "                    pbc=True\n",
+    "                )\n",
+    "\n",
+    "            print(atoms)\n",
+    "\n",
+    "            atoms.calc = calc\n",
+    "\n",
+    "            for i, r in enumerate(tqdm(rs)):\n",
+    "\n",
+    "                if i < skip:\n",
+    "                    continue\n",
+    "\n",
+    "                positions = [\n",
+    "                    [a/2-r/2, a/2, a/2],\n",
+    "                    [a/2+r/2, a/2, a/2],\n",
+    "                ]\n",
+    "\n",
+    "                # atoms.set_initial_magnetic_moments(magmoms)\n",
+    "\n",
+    "                atoms.set_positions(positions)\n",
+    "\n",
+    "                es[i] = atoms.get_potential_energy()\n",
+    "\n",
+    "                write(traj_fpath, atoms, append=\"a\")\n",
+    "        except Exception as e:\n",
+    "            print(e)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a0ac2c09-370b-4fdd-bf74-ea5c4ade0215",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========== MACE-MP(M) ==========\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd91fe7d9c4c455c80e8324bd9926ef8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/118 [00:00<?, ?it/s]"
+      ]
      },
-     "090b1e5649984f0d93bd36d269a149ff": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_275368/4160231063.py:111: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+      "/tmp/ipykernel_275368/4160231063.py:103: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  \"tortuosity\": etv / (abs(es[0] - es.min()) + (es[-1] - es.min())),\n",
+      "/tmp/ipykernel_275368/4160231063.py:105: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+      "  \"spearman-descending-force\": stats.spearmanr(rs[iminf:], fs[iminf:]).statistic,\n",
+      "/tmp/ipykernel_275368/4160231063.py:107: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+      "  \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========== CHGNet ==========\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e26d83ceeac4484c92679cc26f7c41a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/118 [00:00<?, ?it/s]"
+      ]
      },
-     "092372cecdf249b69bdc793b5db4702e": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_275368/4160231063.py:111: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========== M3GNet ==========\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac04367f9be54d34a4f07b69ab4850ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/118 [00:00<?, ?it/s]"
+      ]
      },
-     "09602b3d8ccb43c9b9ea92a8f026f589": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_275368/4160231063.py:111: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+      "/tmp/ipykernel_275368/4160231063.py:106: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+      "  \"spearman-ascending-force\": stats.spearmanr(rs[:iminf], fs[:iminf]).statistic,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========== ORB ==========\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5ecf455670d44f095423a83166c2127",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/118 [00:00<?, ?it/s]"
+      ]
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-        "IPY_MODEL_0017deb8473c44198139ed411461dbdd"
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-       "layout": "IPY_MODEL_8ef9fb7591ab443ca0c6d7d67d59e909"
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-       "layout": "IPY_MODEL_2bae17ad761d4f339134b60574c69cdc",
-       "max": 612,
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-       "max": 472,
-       "style": "IPY_MODEL_399c2ce02f3a44cbb1c3b810c9fba8b3",
-       "value": 472
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-      "state": {
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-     "0a3e24b1dfac42b1a1a3a0afa7c797b8": {
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-      "model_module_version": "2.0.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "description_width": ""
-      }
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_275368/4160231063.py:111: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========== SevenNet ==========\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bff0a28cba234f318bcfac953af5e451",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/118 [00:00<?, ?it/s]"
+      ]
      },
-     "0a51985a38f04160a5167776be11cf29": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
-      "model_name": "LayoutModel",
-      "state": {}
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_275368/4160231063.py:111: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+      "/tmp/ipykernel_275368/4160231063.py:108: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+      "  \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========== EquiformerV2(OC22) ==========\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed1abfe116eb46c79f1692f1081179a1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/118 [00:00<?, ?it/s]"
+      ]
      },
-     "0a7286d8879f46c99fe49be1a875a0d8": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "HTMLStyleModel",
-      "state": {
-       "description_width": "",
-       "font_size": null,
-       "text_color": null
-      }
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_275368/4160231063.py:111: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+      "/tmp/ipykernel_275368/4160231063.py:103: RuntimeWarning: invalid value encountered in scalar divide\n",
+      "  \"tortuosity\": etv / (abs(es[0] - es.min()) + (es[-1] - es.min())),\n",
+      "/tmp/ipykernel_275368/4160231063.py:107: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+      "  \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n",
+      "/tmp/ipykernel_275368/4160231063.py:108: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+      "  \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n",
+      "/tmp/ipykernel_275368/4160231063.py:103: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "  \"tortuosity\": etv / (abs(es[0] - es.min()) + (es[-1] - es.min())),\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========== EquiformerV2(OC20) ==========\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc3ded4031c64ce3bc391a4cf6588d33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/118 [00:00<?, ?it/s]"
+      ]
      },
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_275368/4160231063.py:111: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+      "/tmp/ipykernel_275368/4160231063.py:108: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+      "  \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "for model in MLIPEnum:\n",
+    "    \n",
+    "    model_name = model.name\n",
+    "    \n",
+    "    print(f\"========== {model_name} ==========\")\n",
+    "    \n",
+    "    df = pd.DataFrame(columns=[\n",
+    "        \"name\", \n",
+    "        \"method\", \n",
+    "        \"R\", \"E\", \"F\", \"S^2\", \n",
+    "        \"force-flip-times\",\n",
+    "        \"force-total-variation\",\n",
+    "        \"energy-diff-flip-times\",\n",
+    "        \"energy-grad-norm-max\",\n",
+    "        \"energy-jump\",\n",
+    "        \"energy-total-variation\",\n",
+    "        \"tortuosity\",\n",
+    "        \"conservation-deviation\",\n",
+    "        \"spearman-descending-force\",\n",
+    "        \"spearman-ascending-force\",\n",
+    "        \"spearman-repulsion-energy\",\n",
+    "        \"spearman-attraction-energy\"\n",
+    "    ])\n",
+    "    \n",
+    "\n",
+    "    for symbol in tqdm(chemical_symbols[1:]):\n",
+    "\n",
+    "        da = symbol + symbol\n",
+    "\n",
+    "        out_dir = Path(REGISTRY[model_name][\"family\"]) / da\n",
+    "\n",
+    "        traj_fpath = out_dir / f\"{model_name}.extxyz\"\n",
+    "\n",
+    "\n",
+    "        if traj_fpath.exists():\n",
+    "            traj = read(traj_fpath, index=\":\")\n",
+    "        else:\n",
+    "            continue\n",
+    "\n",
+    "        Rs, Es, Fs, S2s = [], [], [], []\n",
+    "        for atoms in traj:\n",
+    "\n",
+    "            vec = atoms.positions[1] - atoms.positions[0]\n",
+    "            r = np.linalg.norm(vec)\n",
+    "            e = atoms.get_potential_energy()\n",
+    "            f = np.inner(vec/r, atoms.get_forces()[1])\n",
+    "            # s2 = np.mean(np.power(atoms.get_magnetic_moments(), 2))\n",
+    "\n",
+    "            Rs.append(r)\n",
+    "            Es.append(e)\n",
+    "            Fs.append(f)\n",
+    "            # S2s.append(s2)\n",
+    "\n",
+    "        rs = np.array(Rs)\n",
+    "        es = np.array(Es)\n",
+    "        fs = np.array(Fs)\n",
+    "\n",
+    "        indices = np.argsort(rs)[::-1]\n",
+    "        rs = rs[indices]\n",
+    "        es = es[indices]\n",
+    "        fs = fs[indices]\n",
+    "\n",
+    "        iminf = np.argmin(fs)\n",
+    "        imine = np.argmin(es)\n",
+    "\n",
+    "        de_dr = np.gradient(es, rs)\n",
+    "        d2e_dr2 = np.gradient(de_dr, rs)\n",
+    "\n",
+    "        rounded_fs = np.copy(fs)\n",
+    "        rounded_fs[np.abs(rounded_fs) < 1e-2] = 0\n",
+    "        fs_sign = np.sign(rounded_fs)\n",
+    "        fs_sign = fs_sign[fs_sign != 0]\n",
+    "\n",
+    "        # rounded_ediff = np.diff(es)\n",
+    "        # rounded_ediff[np.abs(rounded_ediff) < zero_threshold] = 0\n",
+    "        ediff = np.diff(es)\n",
+    "        ediff[np.abs(ediff) < 1e-3] = 0\n",
+    "        ediff_sign = np.sign(ediff)\n",
+    "        mask = ediff_sign != 0\n",
+    "        ediff = ediff[mask]\n",
+    "        ediff_sign = ediff_sign[mask]\n",
+    "        ediff_flip = np.diff(ediff_sign) != 0\n",
+    "        ejump = np.abs(ediff[:-1][ediff_flip]).sum() + np.abs(ediff[1:][ediff_flip]).sum()\n",
+    "\n",
+    "        conservation_deviation = np.mean(np.abs(fs + de_dr))\n",
+    "        \n",
+    "        etv = np.sum(np.abs(np.diff(es)))\n",
+    "\n",
+    "        data = {\n",
+    "            \"name\": da,\n",
+    "            \"method\": model_name,\n",
+    "            \"R\": rs,\n",
+    "            \"E\": es,\n",
+    "            \"F\": fs,\n",
+    "            \"S^2\": S2s,\n",
+    "            \"force-flip-times\": np.sum(np.diff(fs_sign)!=0),\n",
+    "            \"force-total-variation\": np.sum(np.abs(np.diff(fs))),\n",
+    "            \"energy-diff-flip-times\": np.sum(ediff_flip),\n",
+    "            \"energy-grad-norm-max\": np.max(np.abs(de_dr)),\n",
+    "            \"energy-jump\": ejump,\n",
+    "            # \"energy-grad-norm-mean\": np.mean(de_dr_abs),\n",
+    "            \"energy-total-variation\": etv,\n",
+    "            \"tortuosity\": etv / (abs(es[0] - es.min()) + (es[-1] - es.min())),\n",
+    "            \"conservation-deviation\": conservation_deviation,\n",
+    "            \"spearman-descending-force\": stats.spearmanr(rs[iminf:], fs[iminf:]).statistic,\n",
+    "            \"spearman-ascending-force\": stats.spearmanr(rs[:iminf], fs[:iminf]).statistic,\n",
+    "            \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n",
+    "            \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n",
+    "        }\n",
+    "\n",
+    "        df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+    "\n",
+    "\n",
+    "    json_fpath = Path(REGISTRY[model_name][\"family\"]) / \"homonuclear-diatomics.json\"\n",
+    "\n",
+    "    if json_fpath.exists():\n",
+    "        df0 = pd.read_json(json_fpath)\n",
+    "        df = pd.concat([df0, df], ignore_index=True)\n",
+    "        df.drop_duplicates(inplace=True, subset=[\"name\", \"method\"], keep='last')\n",
+    "\n",
+    "    df.to_json(json_fpath, orient=\"records\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e0dd4367-3dca-440f-a7a9-7fdd84183f2c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>method</th>\n",
+       "      <th>R</th>\n",
+       "      <th>E</th>\n",
+       "      <th>F</th>\n",
+       "      <th>S^2</th>\n",
+       "      <th>force-flip-times</th>\n",
+       "      <th>force-total-variation</th>\n",
+       "      <th>energy-diff-flip-times</th>\n",
+       "      <th>energy-grad-norm-max</th>\n",
+       "      <th>energy-jump</th>\n",
+       "      <th>energy-total-variation</th>\n",
+       "      <th>conservation-deviation</th>\n",
+       "      <th>spearman-descending-force</th>\n",
+       "      <th>spearman-ascending-force</th>\n",
+       "      <th>spearman-repulsion-energy</th>\n",
+       "      <th>spearman-attraction-energy</th>\n",
+       "      <th>tortuosity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>HH</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[3.7199999999999998, 3.70996794, 3.69993586, 3...</td>\n",
+       "      <td>[-1.2249419689178467, -1.2238645553588867, -1....</td>\n",
+       "      <td>[1.91e-06, 0.00826454, 0.00533009, -0.00355052...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>29</td>\n",
+       "      <td>443.614255</td>\n",
+       "      <td>30</td>\n",
+       "      <td>40.656501</td>\n",
+       "      <td>4.074248</td>\n",
+       "      <td>13.676080</td>\n",
+       "      <td>2.022960</td>\n",
+       "      <td>-0.200684</td>\n",
+       "      <td>-0.119952</td>\n",
+       "      <td>-0.986572</td>\n",
+       "      <td>0.844786</td>\n",
+       "      <td>1.797762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>HeHe</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[4.433, 4.4229736200000005, 4.41294724, 4.4029...</td>\n",
+       "      <td>[2.4748411178588867, 2.4748411178588867, 2.474...</td>\n",
+       "      <td>[0.0, -1e-08, 0.0, 0.0, 1e-08, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1448.979436</td>\n",
+       "      <td>43</td>\n",
+       "      <td>160.175544</td>\n",
+       "      <td>13.831072</td>\n",
+       "      <td>32.544741</td>\n",
+       "      <td>4.849522</td>\n",
+       "      <td>-0.021494</td>\n",
+       "      <td>-0.195001</td>\n",
+       "      <td>-0.720218</td>\n",
+       "      <td>0.609519</td>\n",
+       "      <td>3.921846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>LiLi</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[6.572000000000001, 6.561981520000001, 6.55196...</td>\n",
+       "      <td>[-0.21738338470458984, -0.21738338470458984, -...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1127.749591</td>\n",
+       "      <td>20</td>\n",
+       "      <td>281.659136</td>\n",
+       "      <td>2.067203</td>\n",
+       "      <td>20.053466</td>\n",
+       "      <td>2.469147</td>\n",
+       "      <td>-0.714286</td>\n",
+       "      <td>-0.444127</td>\n",
+       "      <td>-0.991010</td>\n",
+       "      <td>-0.654715</td>\n",
+       "      <td>1.386696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>BeBe</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[6.138000000000001, 6.12797338, 6.117946759999...</td>\n",
+       "      <td>[2.665587902069092, 2.665587902069092, 2.66558...</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 1e-08, 0.0, 0.0, 0.0, 0.0...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>25</td>\n",
+       "      <td>1645.960357</td>\n",
+       "      <td>21</td>\n",
+       "      <td>145.040462</td>\n",
+       "      <td>9.422094</td>\n",
+       "      <td>51.647955</td>\n",
+       "      <td>5.076523</td>\n",
+       "      <td>0.544090</td>\n",
+       "      <td>-0.158055</td>\n",
+       "      <td>-0.989962</td>\n",
+       "      <td>0.342476</td>\n",
+       "      <td>1.715953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>BB</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[5.921000000000001, 5.91097088, 5.900941739999...</td>\n",
+       "      <td>[0.6220548152923584, 0.6220548152923584, 0.622...</td>\n",
+       "      <td>[0.0, 0.0, 1e-08, 0.0, 0.0, 1e-08, -1e-08, -1e...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1811.413732</td>\n",
+       "      <td>36</td>\n",
+       "      <td>131.340791</td>\n",
+       "      <td>13.088368</td>\n",
+       "      <td>52.691856</td>\n",
+       "      <td>5.551787</td>\n",
+       "      <td>0.052632</td>\n",
+       "      <td>-0.171460</td>\n",
+       "      <td>-0.985024</td>\n",
+       "      <td>0.757880</td>\n",
+       "      <td>1.617372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>FlFl</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
+       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
+       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>39</td>\n",
+       "      <td>3329.770831</td>\n",
+       "      <td>44</td>\n",
+       "      <td>181.338142</td>\n",
+       "      <td>12.292844</td>\n",
+       "      <td>84.239434</td>\n",
+       "      <td>8.151059</td>\n",
+       "      <td>-0.249255</td>\n",
+       "      <td>-0.316556</td>\n",
+       "      <td>-0.946770</td>\n",
+       "      <td>0.159576</td>\n",
+       "      <td>2.694147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>McMc</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
+       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
+       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>39</td>\n",
+       "      <td>3329.770831</td>\n",
+       "      <td>44</td>\n",
+       "      <td>181.338142</td>\n",
+       "      <td>12.292844</td>\n",
+       "      <td>84.239434</td>\n",
+       "      <td>8.151059</td>\n",
+       "      <td>-0.249255</td>\n",
+       "      <td>-0.316556</td>\n",
+       "      <td>-0.946770</td>\n",
+       "      <td>0.159576</td>\n",
+       "      <td>2.694147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>LvLv</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
+       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
+       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>39</td>\n",
+       "      <td>3329.770831</td>\n",
+       "      <td>44</td>\n",
+       "      <td>181.338142</td>\n",
+       "      <td>12.292844</td>\n",
+       "      <td>84.239434</td>\n",
+       "      <td>8.151059</td>\n",
+       "      <td>-0.249255</td>\n",
+       "      <td>-0.316556</td>\n",
+       "      <td>-0.946770</td>\n",
+       "      <td>0.159576</td>\n",
+       "      <td>2.694147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>TsTs</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
+       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
+       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>39</td>\n",
+       "      <td>3329.770831</td>\n",
+       "      <td>44</td>\n",
+       "      <td>181.338142</td>\n",
+       "      <td>12.292844</td>\n",
+       "      <td>84.239434</td>\n",
+       "      <td>8.151059</td>\n",
+       "      <td>-0.249255</td>\n",
+       "      <td>-0.316556</td>\n",
+       "      <td>-0.946770</td>\n",
+       "      <td>0.159576</td>\n",
+       "      <td>2.694147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>OgOg</td>\n",
+       "      <td>ALIGNN</td>\n",
+       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
+       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
+       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>39</td>\n",
+       "      <td>3329.770831</td>\n",
+       "      <td>44</td>\n",
+       "      <td>181.338142</td>\n",
+       "      <td>12.292844</td>\n",
+       "      <td>84.239434</td>\n",
+       "      <td>8.151059</td>\n",
+       "      <td>-0.249255</td>\n",
+       "      <td>-0.316556</td>\n",
+       "      <td>-0.946770</td>\n",
+       "      <td>0.159576</td>\n",
+       "      <td>2.694147</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>118 rows × 18 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     name  method                                                  R  \\\n",
+       "118    HH  ALIGNN  [3.7199999999999998, 3.70996794, 3.69993586, 3...   \n",
+       "119  HeHe  ALIGNN  [4.433, 4.4229736200000005, 4.41294724, 4.4029...   \n",
+       "120  LiLi  ALIGNN  [6.572000000000001, 6.561981520000001, 6.55196...   \n",
+       "121  BeBe  ALIGNN  [6.138000000000001, 6.12797338, 6.117946759999...   \n",
+       "122    BB  ALIGNN  [5.921000000000001, 5.91097088, 5.900941739999...   \n",
+       "..    ...     ...                                                ...   \n",
+       "231  FlFl  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
+       "232  McMc  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
+       "233  LvLv  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
+       "234  TsTs  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
+       "235  OgOg  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
+       "\n",
+       "                                                     E  \\\n",
+       "118  [-1.2249419689178467, -1.2238645553588867, -1....   \n",
+       "119  [2.4748411178588867, 2.4748411178588867, 2.474...   \n",
+       "120  [-0.21738338470458984, -0.21738338470458984, -...   \n",
+       "121  [2.665587902069092, 2.665587902069092, 2.66558...   \n",
+       "122  [0.6220548152923584, 0.6220548152923584, 0.622...   \n",
+       "..                                                 ...   \n",
+       "231  [-10.127323150634766, -10.127323150634766, -10...   \n",
+       "232  [-10.127323150634766, -10.127323150634766, -10...   \n",
+       "233  [-10.127323150634766, -10.127323150634766, -10...   \n",
+       "234  [-10.127323150634766, -10.127323150634766, -10...   \n",
+       "235  [-10.127323150634766, -10.127323150634766, -10...   \n",
+       "\n",
+       "                                                     F S^2 force-flip-times  \\\n",
+       "118  [1.91e-06, 0.00826454, 0.00533009, -0.00355052...  []               29   \n",
+       "119  [0.0, -1e-08, 0.0, 0.0, 1e-08, 0.0, 0.0, 0.0, ...  []               44   \n",
+       "120  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  []               22   \n",
+       "121  [0.0, 0.0, 0.0, 0.0, 1e-08, 0.0, 0.0, 0.0, 0.0...  []               25   \n",
+       "122  [0.0, 0.0, 1e-08, 0.0, 0.0, 1e-08, -1e-08, -1e...  []               35   \n",
+       "..                                                 ...  ..              ...   \n",
+       "231  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
+       "232  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
+       "233  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
+       "234  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
+       "235  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
+       "\n",
+       "     force-total-variation energy-diff-flip-times  energy-grad-norm-max  \\\n",
+       "118             443.614255                     30             40.656501   \n",
+       "119            1448.979436                     43            160.175544   \n",
+       "120            1127.749591                     20            281.659136   \n",
+       "121            1645.960357                     21            145.040462   \n",
+       "122            1811.413732                     36            131.340791   \n",
+       "..                     ...                    ...                   ...   \n",
+       "231            3329.770831                     44            181.338142   \n",
+       "232            3329.770831                     44            181.338142   \n",
+       "233            3329.770831                     44            181.338142   \n",
+       "234            3329.770831                     44            181.338142   \n",
+       "235            3329.770831                     44            181.338142   \n",
+       "\n",
+       "     energy-jump  energy-total-variation  conservation-deviation  \\\n",
+       "118     4.074248               13.676080                2.022960   \n",
+       "119    13.831072               32.544741                4.849522   \n",
+       "120     2.067203               20.053466                2.469147   \n",
+       "121     9.422094               51.647955                5.076523   \n",
+       "122    13.088368               52.691856                5.551787   \n",
+       "..           ...                     ...                     ...   \n",
+       "231    12.292844               84.239434                8.151059   \n",
+       "232    12.292844               84.239434                8.151059   \n",
+       "233    12.292844               84.239434                8.151059   \n",
+       "234    12.292844               84.239434                8.151059   \n",
+       "235    12.292844               84.239434                8.151059   \n",
+       "\n",
+       "     spearman-descending-force  spearman-ascending-force  \\\n",
+       "118                  -0.200684                 -0.119952   \n",
+       "119                  -0.021494                 -0.195001   \n",
+       "120                  -0.714286                 -0.444127   \n",
+       "121                   0.544090                 -0.158055   \n",
+       "122                   0.052632                 -0.171460   \n",
+       "..                         ...                       ...   \n",
+       "231                  -0.249255                 -0.316556   \n",
+       "232                  -0.249255                 -0.316556   \n",
+       "233                  -0.249255                 -0.316556   \n",
+       "234                  -0.249255                 -0.316556   \n",
+       "235                  -0.249255                 -0.316556   \n",
+       "\n",
+       "     spearman-repulsion-energy  spearman-attraction-energy  tortuosity  \n",
+       "118                  -0.986572                    0.844786    1.797762  \n",
+       "119                  -0.720218                    0.609519    3.921846  \n",
+       "120                  -0.991010                   -0.654715    1.386696  \n",
+       "121                  -0.989962                    0.342476    1.715953  \n",
+       "122                  -0.985024                    0.757880    1.617372  \n",
+       "..                         ...                         ...         ...  \n",
+       "231                  -0.946770                    0.159576    2.694147  \n",
+       "232                  -0.946770                    0.159576    2.694147  \n",
+       "233                  -0.946770                    0.159576    2.694147  \n",
+       "234                  -0.946770                    0.159576    2.694147  \n",
+       "235                  -0.946770                    0.159576    2.694147  \n",
+       "\n",
+       "[118 rows x 18 columns]"
+      ]
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