{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3200850a-b8fb-4f50-9815-16ae8da0f942",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ase import Atom, Atoms\n",
    "from ase.data import chemical_symbols, covalent_radii, vdw_alvarez\n",
    "from ase.io import read, write\n",
    "from pymatgen.core import Element\n",
    "from scipy import stats\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from mlip_arena.models.utils import REGISTRY, MLIPEnum\n",
    "\n",
    "# model_name = \"MACE-MP(M)\"\n",
    "\n",
    "# calc = MLIPEnum[model_name].value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90887faa-1601-4c4c-9c44-d16731471d7f",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== 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"
     ]
    },
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/118 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='H2', pbc=True, cell=[7.4399999999999995, 7.441, 7.441999999999999])\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/344 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='He2', pbc=True, cell=[8.866, 8.866999999999999, 8.868])\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/418 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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"
     ]
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/542 [00:00<?, ?it/s]"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/527 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "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": {
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       "model_id": "0b96202a2c594947b6093ecab0d89a25",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/516 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "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"
     ]
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/480 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='N2', pbc=True, cell=[10.292, 10.293, 10.294])\n"
     ]
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/450 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='O2', pbc=True, cell=[9.3, 9.301, 9.302000000000001])\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/405 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='F2', pbc=True, cell=[9.052, 9.052999999999999, 9.054])\n"
     ]
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/401 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='Ne2', pbc=True, cell=[9.796000000000001, 9.797, 9.798000000000002])\n"
     ]
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/437 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "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": {
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       "model_id": "07b8b95e8418423193b3eb48a1c3197a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/625 [00:00<?, ?it/s]"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": {
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/651 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "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"
     ]
    },
    {
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      "text/plain": [
       "  0%|          | 0/588 [00:00<?, ?it/s]"
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    {
     "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"
     ]
    },
    {
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      "text/plain": [
       "  0%|          | 0/578 [00:00<?, ?it/s]"
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    },
    {
     "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"
     ]
    },
    {
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      "text/plain": [
       "  0%|          | 0/492 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='S2', pbc=True, cell=[11.718, 11.719, 11.72])\n"
     ]
    },
    {
     "data": {
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      },
      "text/plain": [
       "  0%|          | 0/491 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='Cl2', pbc=True, cell=[11.284, 11.285, 11.286000000000001])\n"
     ]
    },
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='Ar2', pbc=True, cell=[11.346, 11.347, 11.348])\n"
     ]
    },
    {
     "data": {
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       "model_id": "ebb843c27f634344bd10a526081ca13c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/471 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "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": {
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      "text/plain": [
       "  0%|          | 0/663 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "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": {
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      "text/plain": [
       "  0%|          | 0/653 [00:00<?, ?it/s]"
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    },
    {
     "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": {
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/646 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "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": {
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/618 [00:00<?, ?it/s]"
      ]
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     "metadata": {},
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    },
    {
     "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"
     ]
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/612 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "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"
     ]
    },
    {
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      "text/plain": [
       "  0%|          | 0/634 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "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"
     ]
    },
    {
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       "  0%|          | 0/634 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "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": {
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      "text/plain": [
       "  0%|          | 0/637 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "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"
     ]
    },
    {
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      "text/plain": [
       "  0%|          | 0/630 [00:00<?, ?it/s]"
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    {
     "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"
     ]
    },
    {
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      "text/plain": [
       "  0%|          | 0/632 [00:00<?, ?it/s]"
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    },
    {
     "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"
     ]
    },
    {
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      "text/plain": [
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    },
    {
     "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"
     ]
    },
    {
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      "text/plain": [
       "  0%|          | 0/631 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
    {
     "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"
     ]
    },
    {
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      "text/plain": [
       "  0%|          | 0/609 [00:00<?, ?it/s]"
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    },
    {
     "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"
     ]
    },
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      "Atoms(symbols='As2', pbc=True, cell=[11.655999999999999, 11.656999999999998, 11.658])\n"
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       "  0%|          | 0/475 [00:00<?, ?it/s]"
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      "33 is not in list\n",
      "Atoms(symbols='Se2', pbc=True, cell=[11.284, 11.285, 11.286000000000001])\n"
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       "  0%|          | 0/456 [00:00<?, ?it/s]"
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      "34 is not in list\n",
      "Atoms(symbols='Br2', pbc=True, cell=[11.532000000000002, 11.533000000000001, 11.534000000000002])\n"
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       "  0%|          | 0/468 [00:00<?, ?it/s]"
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      "Atoms(symbols='Kr2', pbc=True, cell=[13.950000000000001, 13.951, 13.952000000000002])\n"
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       "  0%|          | 0/593 [00:00<?, ?it/s]"
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      "Atoms(symbols='Rb2', pbc=True, cell=[19.902, 19.903000000000002, 19.904])\n"
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       "  0%|          | 0/797 [00:00<?, ?it/s]"
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      "Atoms(symbols='Sr2', pbc=True, cell=[17.608, 17.609, 17.61])\n"
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       "  0%|          | 0/704 [00:00<?, ?it/s]"
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      "38 is not in list\n",
      "Atoms(symbols='Y2', pbc=True, cell=[17.05, 17.051000000000002, 17.052])\n"
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       "  0%|          | 0/681 [00:00<?, ?it/s]"
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      "Atoms(symbols='Zr2', pbc=True, cell=[15.624, 15.625, 15.626000000000001])\n"
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       "  0%|          | 0/623 [00:00<?, ?it/s]"
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      "40 is not in list\n",
      "Atoms(symbols='Nb2', pbc=True, cell=[15.872000000000002, 15.873000000000001, 15.874000000000002])\n"
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       "  0%|          | 0/646 [00:00<?, ?it/s]"
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      "Atoms(symbols='Mo2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002])\n"
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       "  0%|          | 0/620 [00:00<?, ?it/s]"
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      "42 is not in list\n",
      "Atoms(symbols='Tc2', pbc=True, cell=[15.128, 15.129, 15.13])\n"
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       "  0%|          | 0/624 [00:00<?, ?it/s]"
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      "43 is not in list\n",
      "Atoms(symbols='Ru2', pbc=True, cell=[15.252, 15.253, 15.254000000000001])\n"
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       "  0%|          | 0/631 [00:00<?, ?it/s]"
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      "44 is not in list\n",
      "Atoms(symbols='Rh2', pbc=True, cell=[15.128, 15.129, 15.13])\n"
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       "  0%|          | 0/628 [00:00<?, ?it/s]"
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      "45 is not in list\n",
      "Atoms(symbols='Pd2', pbc=True, cell=[13.33, 13.331, 13.332])\n"
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       "  0%|          | 0/541 [00:00<?, ?it/s]"
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      "46 is not in list\n",
      "Atoms(symbols='Ag2', pbc=True, cell=[15.686, 15.687, 15.688])\n"
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       "  0%|          | 0/653 [00:00<?, ?it/s]"
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    {
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     "text": [
      "47 is not in list\n",
      "Atoms(symbols='Cd2', pbc=True, cell=[15.438000000000002, 15.439000000000002, 15.440000000000003])\n"
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       "  0%|          | 0/642 [00:00<?, ?it/s]"
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      "48 is not in list\n",
      "Atoms(symbols='In2', pbc=True, cell=[15.066, 15.067, 15.068000000000001])\n"
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       "  0%|          | 0/625 [00:00<?, ?it/s]"
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     "text": [
      "49 is not in list\n",
      "Atoms(symbols='Sn2', pbc=True, cell=[15.004, 15.004999999999999, 15.006])\n"
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       "  0%|          | 0/625 [00:00<?, ?it/s]"
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     "text": [
      "50 is not in list\n",
      "Atoms(symbols='Sb2', pbc=True, cell=[15.314000000000002, 15.315000000000001, 15.316000000000003])\n"
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       "  0%|          | 0/640 [00:00<?, ?it/s]"
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     "text": [
      "51 is not in list\n",
      "Atoms(symbols='Te2', pbc=True, cell=[12.338000000000001, 12.339, 12.340000000000002])\n"
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       "  0%|          | 0/492 [00:00<?, ?it/s]"
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     "text": [
      "52 is not in list\n",
      "Atoms(symbols='I2', pbc=True, cell=[12.648000000000001, 12.649000000000001, 12.650000000000002])\n"
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       "  0%|          | 0/507 [00:00<?, ?it/s]"
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     "metadata": {},
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     "text": [
      "Atoms(symbols='Xe2', pbc=True, cell=[12.772, 12.773, 12.774000000000001])\n"
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       "  0%|          | 0/512 [00:00<?, ?it/s]"
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    {
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     "text": [
      "54 is not in list\n",
      "Atoms(symbols='Cs2', pbc=True, cell=[21.576, 21.577, 21.578])\n"
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       "  0%|          | 0/859 [00:00<?, ?it/s]"
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    {
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     "text": [
      "55 is not in list\n",
      "Atoms(symbols='Ba2', pbc=True, cell=[18.785999999999998, 18.787, 18.787999999999997])\n"
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       "  0%|          | 0/745 [00:00<?, ?it/s]"
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    {
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     "text": [
      "56 is not in list\n",
      "Atoms(symbols='La2', pbc=True, cell=[18.476, 18.477, 18.477999999999998])\n"
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       "  0%|          | 0/737 [00:00<?, ?it/s]"
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     "text": [
      "57 is not in list\n",
      "Atoms(symbols='Ce2', pbc=True, cell=[17.855999999999998, 17.857, 17.857999999999997])\n"
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       "  0%|          | 0/709 [00:00<?, ?it/s]"
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      "58 is not in list\n",
      "Atoms(symbols='Pr2', pbc=True, cell=[18.104, 18.105, 18.105999999999998])\n"
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       "  0%|          | 0/722 [00:00<?, ?it/s]"
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     "text": [
      "59 is not in list\n",
      "Atoms(symbols='Nd2', pbc=True, cell=[18.290000000000003, 18.291000000000004, 18.292])\n"
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       "  0%|          | 0/733 [00:00<?, ?it/s]"
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     "text": [
      "60 is not in list\n",
      "Atoms(symbols='Pm2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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       "  0%|          | 0/420 [00:00<?, ?it/s]"
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     "text": [
      "61 is not in list\n",
      "Atoms(symbols='Sm2', pbc=True, cell=[17.98, 17.981, 17.982])\n"
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       "  0%|          | 0/720 [00:00<?, ?it/s]"
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     "text": [
      "62 is not in list\n",
      "Atoms(symbols='Eu2', pbc=True, cell=[17.794, 17.795, 17.796])\n"
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       "  0%|          | 0/711 [00:00<?, ?it/s]"
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     "text": [
      "63 is not in list\n",
      "Atoms(symbols='Gd2', pbc=True, cell=[17.546, 17.547, 17.548])\n"
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       "  0%|          | 0/700 [00:00<?, ?it/s]"
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     "text": [
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      "Atoms(symbols='Tb2', pbc=True, cell=[17.298000000000002, 17.299000000000003, 17.3])\n"
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       "  0%|          | 0/690 [00:00<?, ?it/s]"
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      "65 is not in list\n",
      "Atoms(symbols='Dy2', pbc=True, cell=[17.794, 17.795, 17.796])\n"
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       "  0%|          | 0/716 [00:00<?, ?it/s]"
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      "Atoms(symbols='Ho2', pbc=True, cell=[17.422, 17.423000000000002, 17.424])\n"
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       "  0%|          | 0/474 [00:00<?, ?it/s]"
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       "  0%|          | 0/465 [00:00<?, ?it/s]"
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       "  0%|          | 0/465 [00:00<?, ?it/s]"
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       "  0%|          | 0/401 [00:00<?, ?it/s]"
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       "  0%|          | 0/674 [00:00<?, ?it/s]"
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       "  0%|          | 0/722 [00:00<?, ?it/s]"
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       "  0%|          | 0/712 [00:00<?, ?it/s]"
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       "  0%|          | 0/663 [00:00<?, ?it/s]"
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       "  0%|          | 0/703 [00:00<?, ?it/s]"
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       "  0%|          | 0/702 [00:00<?, ?it/s]"
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       "  0%|          | 0/715 [00:00<?, ?it/s]"
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       "  0%|          | 0/793 [00:00<?, ?it/s]"
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       "  0%|          | 0/1036 [00:00<?, ?it/s]"
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       "  0%|          | 0/927 [00:00<?, ?it/s]"
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       "  0%|          | 0/819 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101 is not in list\n",
      "Atoms(symbols='No2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2648f8226f2439ab06cb1123091d96e",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102 is not in list\n",
      "Atoms(symbols='Lr2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57d9a32acc164368b6454a93d1af32d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103 is not in list\n",
      "Atoms(symbols='Rf2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abc295ec44e947dabbecd002950da314",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104 is not in list\n",
      "Atoms(symbols='Db2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea6ef834e6fc44fcbf6b1ab44ec379e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105 is not in list\n",
      "Atoms(symbols='Sg2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aaa35c274b08467e86b690c9697aedb7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "106 is not in list\n",
      "Atoms(symbols='Bh2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04a9d6f79b344f2d836ebd22e38cf5aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107 is not in list\n",
      "Atoms(symbols='Hs2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "747b6cb9ffd045ffb64cb3e2295bea06",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108 is not in list\n",
      "Atoms(symbols='Mt2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5deb50644d58476cbe6b68866c6b513f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "109 is not in list\n",
      "Atoms(symbols='Ds2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c18cd8b6069e4fda917ab388fa8895a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "110 is not in list\n",
      "Atoms(symbols='Rg2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54d0eaa9374e49af9bfa944f11dd944b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111 is not in list\n",
      "Atoms(symbols='Cn2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9062ff7ef8004a91a83143148f6be4c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112 is not in list\n",
      "Atoms(symbols='Nh2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6484bf80eb4445a83d0fbda39d96c76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113 is not in list\n",
      "Atoms(symbols='Fl2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6d31a7f18364512b2c9d6c5a40fa427",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114 is not in list\n",
      "Atoms(symbols='Mc2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba8206f8afa74c68a68fb6c093c52e46",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115 is not in list\n",
      "Atoms(symbols='Lv2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ece8574e260446c593992e1acf7d10ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca585859fce94007b174054465be99be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2730cd949b314eb4b9c7e01d383d0940",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "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]"
      ]
     },
     "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]"
      ]
     },
     "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]"
      ]
     },
     "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]"
      ]
     },
     "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]"
      ]
     },
     "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]"
      ]
     },
     "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
      },
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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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== eSCN(OC20) ==========\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e01d03146a9d48b39453c7d009f351b9",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== MACE-OFF(M) ==========\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2db61130b304bbf9a9663f7816b4cb7",
       "version_major": 2,
       "version_minor": 0
      },
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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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== ALIGNN ==========\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5548223541194414821c0884c79d50df",
       "version_major": 2,
       "version_minor": 0
      },
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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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