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": 3, "id": "3200850a-b8fb-4f50-9815-16ae8da0f942", "metadata": { "tags": [] @@ -21,13 +21,9 @@ "from scipy import stats\n", "from tqdm.auto import tqdm\n", "\n", - "from mlip_arena.models.utils import REGISTRY, MLIPEnum\n", - "\n", - "%matplotlib inline\n", + "from mlip_arena.models import MLIPEnum, REGISTRY\n", "\n", - "# model_name = \"MACE-MP(M)\"\n", - "\n", - "# calc = MLIPEnum[model_name].value()" + "%matplotlib inline" ] }, { @@ -43,23 +39,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "========== eSCN(OC20) ==========\n" + "========== eqV2(OMat) ==========\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/zitnick/ocp_logs/4486283/30683411_submitted.pkl'\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" + "WARNING:root:Couldn't modify the submission pickle with error: [Errno 2] No such file or directory: '/fsx-ocp-med/lbluque/logs/omat-alex-mp/S2EFS/train/4460394/32006266_submitted.pkl'\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24eab286853845a9adc00bf1b005fe96", + "model_id": "e77f2270a8f34d81b65cab9852bab94c", "version_major": 2, "version_minor": 0 }, @@ -74,13 +67,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Atoms(symbols='H2', pbc=True, cell=[7.4399999999999995, 7.441, 7.441999999999999])\n" + "Atoms(symbols='H2', pbc=True, cell=[7.4399999999999995, 7.441, 7.441999999999999], calculator=SinglePointCalculator(...))\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ccd4691d8444d92aee01824ac31c279", + "model_id": "33161320d6e5493ab57a0848eb1395a5", "version_major": 2, "version_minor": 0 }, @@ -95,13 +88,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Atoms(symbols='He2', pbc=True, cell=[8.866, 8.866999999999999, 8.868])\n" + "Atoms(symbols='He2', pbc=True, cell=[8.866, 8.866999999999999, 8.868], calculator=SinglePointCalculator(...))\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "021844151e0748cab81beb127dce87d9", + "model_id": "4109a21a613a448a94f4667dd7383747", "version_major": 2, "version_minor": 0 }, @@ -122,7 +115,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0351d3f8e0234361ae62ab84d59a625a", + "model_id": "5c0588112db2428bac40c25c24530e67", "version_major": 2, "version_minor": 0 }, @@ -143,7 +136,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1af3ab5e3cfa470597b1b0b00f4ea89e", + "model_id": "cc5d8bcc3a49475fb01984ca4c768bee", "version_major": 2, "version_minor": 0 }, @@ -164,7 +157,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd070b6565634a87bac75970ac988db9", + "model_id": "66d7e64e73394dc39a66ab3596fec3ca", "version_major": 2, "version_minor": 0 }, @@ -185,7 +178,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ad1a090228244cd89a40f0dcae83baac", + "model_id": "8a6fa1e6419b4987ac7430e62b76234d", "version_major": 2, "version_minor": 0 }, @@ -206,7 +199,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2d15b033ce7e463ca30c1edf770e554d", + "model_id": "ef48b0b0e9904154823ab0d01bd44376", "version_major": 2, "version_minor": 0 }, @@ -227,7 +220,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a47ba796ac0d43f49bb9a6a372533408", + "model_id": "cafa0ec9fa784f25a2f64d8c76496593", "version_major": 2, "version_minor": 0 }, @@ -248,7 +241,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "641dfc50acee41d89321000de0472249", + "model_id": "21d27eaeee9848798b9f819da5326c05", "version_major": 2, "version_minor": 0 }, @@ -269,7 +262,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0fb90f08d0114d66b42e277570075867", + "model_id": "f1a5036eebaa4e7c9601bc2ded6e7347", "version_major": 2, "version_minor": 0 }, @@ -290,7 +283,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e301092e4a441f9b1bd88b0612d4fa7", + "model_id": "759939e385084be58faa4e41c6cc2648", "version_major": 2, "version_minor": 0 }, @@ -311,7 +304,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3da0910f4f7648c4a2698da360d606c7", + "model_id": "96a4ffb0515f45539e6de783c39cd398", "version_major": 2, "version_minor": 0 }, @@ -332,7 +325,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4fb4bcc42863455698e74a6543d13797", + "model_id": "592e2b487edd47c1b0a4165064533689", "version_major": 2, "version_minor": 0 }, @@ -353,7 +346,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7973fdc719574ca2ab1097efacc35bfb", + "model_id": "4f5ee68259a64c76beda1adc163a6cbd", "version_major": 2, "version_minor": 0 }, @@ -374,7 +367,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dccf29b383d64c0cb120c8f72763bd98", + "model_id": "cb5f6ecb3ca74d50b75430a0c388ad6f", "version_major": 2, "version_minor": 0 }, @@ -395,7 +388,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea09c6aac7d84c339dbaddda3853b9e2", + "model_id": "25b6bd606d7347ccbbc065dbac275f71", "version_major": 2, "version_minor": 0 }, @@ -416,7 +409,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5026400e316b4f9db2f641893c267440", + "model_id": "416c99cd3f864f419cd0184960a4155b", "version_major": 2, "version_minor": 0 }, @@ -437,7 +430,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "017a69b355da4410ae25801d630ac39b", + "model_id": "6bf463d79e45467bb5b64a129a86e03e", "version_major": 2, "version_minor": 0 }, @@ -458,7 +451,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d1e6e0bb1274281a3c4923583d5ef26", + "model_id": "e0197e568746415eb890d89cd0bbeaa3", "version_major": 2, "version_minor": 0 }, @@ -479,7 +472,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83db27472a2b4b7c90b0ec38981cf8a0", + "model_id": "3acd1fa6108947b2bb14edf0999c89ab", "version_major": 2, "version_minor": 0 }, @@ -500,7 +493,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "282ef294f8a442068d4a29e7b7c28333", + "model_id": "bec7223da3404c3a9da1cffeba23286f", "version_major": 2, "version_minor": 0 }, @@ -521,7 +514,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3afef0afbfaa49438e4aa91c171db434", + "model_id": "b25ebdf58a274614a1cecf32328f5164", "version_major": 2, "version_minor": 0 }, @@ -542,7 +535,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aaecbf8fa3a04c8098655912e69a6b8d", + "model_id": "c5ce66b45fdd4bac8dd69caf7b6a8a6f", "version_major": 2, "version_minor": 0 }, @@ -563,7 +556,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e70fa07b40274cd78d97888cf325187e", + "model_id": "b094adadc86849238da83396b6e818f3", "version_major": 2, "version_minor": 0 }, @@ -584,7 +577,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c31f32587a7b4785a5ae8fc57a67a6a8", + "model_id": "60b9c56384d1403da19808c5b5410c40", "version_major": 2, "version_minor": 0 }, @@ -605,7 +598,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "56bd67b330db46d1b2c2a061a91382f4", + "model_id": "eb13c83599ac439a886b1b39ab9e3219", "version_major": 2, "version_minor": 0 }, @@ -626,7 +619,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "255f15d5e8e54211b9df002198d5edd2", + "model_id": "74d387a976ac45518667180f5f1d7dca", "version_major": 2, "version_minor": 0 }, @@ -647,7 +640,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7506a894ef4e4837b1be7361c35171d9", + "model_id": "49a1583cdbc649729d9d30ba63c140d3", "version_major": 2, "version_minor": 0 }, @@ -668,7 +661,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9dd100e193014415a7246477fb6510fd", + "model_id": "0e3c31876fed4ecdac47b797c62dc08e", "version_major": 2, "version_minor": 0 }, @@ -689,7 +682,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "62fb70fb1d1d40d080778d91f4e72586", + "model_id": "7f9e6fa0a05c4344bd68cf58f0a9816e", "version_major": 2, "version_minor": 0 }, @@ -710,7 +703,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "904220ce9932428fa052402e69ffdcad", + "model_id": "ad3c78bdf5af4d1fa90eab5f10ba2bde", "version_major": 2, "version_minor": 0 }, @@ -731,7 +724,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ff0fe051c4044ace8fbeb5d2e94db0c6", + "model_id": "a7fdb11ede9544e59987eea70e559d86", "version_major": 2, "version_minor": 0 }, @@ -752,7 +745,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bda6526e6fa04bad9b69112c7b73ada7", + "model_id": "c633a1308e304dae9dda69ad84a96989", "version_major": 2, "version_minor": 0 }, @@ -773,7 +766,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e77fbc41d244195b742bffa2670e945", + "model_id": "34b3b4bb2fbb40df91e7c2444b2ce942", "version_major": 2, "version_minor": 0 }, @@ -794,7 +787,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8789d10a86d843b79ba4523da889f04b", + "model_id": "7ea332ea894447e5ae593a6cb1b56c7a", "version_major": 2, "version_minor": 0 }, @@ -815,7 +808,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0de3c3adc534efd94770361755963a6", + "model_id": "27f59e0b042745b89fa04a0df01201a0", "version_major": 2, "version_minor": 0 }, @@ -836,7 +829,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fd8192ac694d4b3a99ca48302393790e", + "model_id": "10031ad6ac8446be81d9ee42aa7c630e", "version_major": 2, "version_minor": 0 }, @@ -857,7 +850,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3e9cbac78b434f2196bb066f61bba487", + "model_id": "9b33442f8a80462f9e8cc8426fc933e8", "version_major": 2, "version_minor": 0 }, @@ -878,7 +871,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "17e1466cb09e422fb146edd954eb4db9", + "model_id": "964912428a9f4b6888e86e77a056a278", "version_major": 2, "version_minor": 0 }, @@ -889,6 +882,27 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Zr2', pbc=True, cell=[15.624, 15.625, 15.626000000000001])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "99995c462de64ee886cd1ce508ca7b32", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/623 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", @@ -899,7 +913,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58c04052ec954aba8d271d544daa8fbb", + "model_id": "fe0eb64e91bd4f98ac3bfbeb630762ce", "version_major": 2, "version_minor": 0 }, @@ -920,7 +934,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c275743709304330bc3205c17184ca0f", + "model_id": "5a1890a3879a488aa47630c7cb2cb7d3", "version_major": 2, "version_minor": 0 }, @@ -932,21 +946,110 @@ "output_type": "display_data" }, { - "name": "stderr", + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Tc2', pbc=True, cell=[15.128, 15.129, 15.13])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "76815ec05cc4495cbf8ce252865fbdc5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/624 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Ru2', pbc=True, cell=[15.252, 15.253, 15.254000000000001])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e5885834967a44d8927faad4458ce7c6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/631 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Rh2', pbc=True, cell=[15.128, 15.129, 15.13])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9ab9223fdc0540c890ec01b16c6e2b7f", + "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": [ + "Atoms(symbols='Pd2', pbc=True, cell=[13.33, 13.331, 13.332])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d3396aed00b744d0afb663cc50d721d4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/541 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", "output_type": "stream", "text": [ - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" + "Atoms(symbols='Ag2', pbc=True, cell=[15.686, 15.687, 15.688])\n" ] }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1b45e3364bca474ebbe35e3151e95f5b", + "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", @@ -957,7 +1060,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3d2c83f5b55c453db1a23a3d99bb3344", + "model_id": "375c045215b247bba8adc37561f8073c", "version_major": 2, "version_minor": 0 }, @@ -978,7 +1081,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3fad57be74d84a4bb10810043f9199a4", + "model_id": "223a9efce99d4fef9ae48b6a2b5d2973", "version_major": 2, "version_minor": 0 }, @@ -999,7 +1102,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a07721be340f4731a315ce7741391683", + "model_id": "d46cec3a59b04c28b09c53565784ed1b", "version_major": 2, "version_minor": 0 }, @@ -1020,7 +1123,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4d72e3d97a07494eb35b3b2e1ddfd1ec", + "model_id": "ae2e54fb70d849aea94f03960e144bc0", "version_major": 2, "version_minor": 0 }, @@ -1041,7 +1144,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6133cef6ebcb4f1d8eb0cf4b4b1c6741", + "model_id": "556beb20e06b4494b4a9cedecc7d1bb2", "version_major": 2, "version_minor": 0 }, @@ -1062,7 +1165,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3bd8abaf4d7a420ba8803fbb192a2e1a", + "model_id": "580220c5d9f84ddeba2d107cebc27388", "version_major": 2, "version_minor": 0 }, @@ -1083,7 +1186,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "db6b9d25f3c6447bb616b456f860eafb", + "model_id": "b223a63423b84d1b9aed015ab28aa74d", "version_major": 2, "version_minor": 0 }, @@ -1104,7 +1207,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1ea6aa7e55a24d03a56c4baef169c2c5", + "model_id": "222cba83b3f244de88155582bea72e79", "version_major": 2, "version_minor": 0 }, @@ -1125,7 +1228,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef6bd3aac5c841b484873ec94326b59f", + "model_id": "cdcb3bdf95c84fa5a9fcd1aadbc360f5", "version_major": 2, "version_minor": 0 }, @@ -1146,7 +1249,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "603378b57ec640cdba20b9400027d995", + "model_id": "59f93f3830504b5c8b1f31fdcb271391", "version_major": 2, "version_minor": 0 }, @@ -1167,7 +1270,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c3286c2110794defb8e5e8eb60a860c6", + "model_id": "c3ab174f1c404215b8a1fdf75826fd2e", "version_major": 2, "version_minor": 0 }, @@ -1188,7 +1291,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7b4dc48b02af425b99d44b6d5dcc663a", + "model_id": "b2c80e8cf5c54724a3f51476ddf35cb1", "version_major": 2, "version_minor": 0 }, @@ -1209,7 +1312,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e24310aa6f854226a4d49044b328855c", + "model_id": "c10a80c6609b4ca3886b14f604b9c34d", "version_major": 2, "version_minor": 0 }, @@ -1230,7 +1333,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c296999dc304301afa71043656e6aa5", + "model_id": "e50e5736fd21439f9b3f09840ee7fadf", "version_major": 2, "version_minor": 0 }, @@ -1251,7 +1354,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "578da66d66f347ce9965d01fb97d1dfe", + "model_id": "7cbbcac8168c4d319740457057076c32", "version_major": 2, "version_minor": 0 }, @@ -1272,7 +1375,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e79a78a61d0f4197a5407034ca7bbe7c", + "model_id": "7f474e7fa0b94820946b87a09daf58d2", "version_major": 2, "version_minor": 0 }, @@ -1293,7 +1396,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a69ead578b04443aa57b3469ab1a607a", + "model_id": "3020aa47f4fa4bf6bb584ab08c95c107", "version_major": 2, "version_minor": 0 }, @@ -1314,7 +1417,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa6237744d0b45e392a580572436f57f", + "model_id": "d19efa9854514dcb9f29e97e6304438c", "version_major": 2, "version_minor": 0 }, @@ -1335,7 +1438,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34c71c7acb4e45a9932124173ee455e3", + "model_id": "58edf1da064843ebb33b8913e1ae5203", "version_major": 2, "version_minor": 0 }, @@ -1356,7 +1459,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "172541ffaf77413eaaf308501eed8f79", + "model_id": "0f12b385965a46efbc9478258c1cbd39", "version_major": 2, "version_minor": 0 }, @@ -1377,7 +1480,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b5629e3b5e346d9b69a7017ddaa0be6", + "model_id": "50a7a50929a64085b40c4f1a603a3ac4", "version_major": 2, "version_minor": 0 }, @@ -1398,7 +1501,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48ba2827991c4f319c8b136d179ba09e", + "model_id": "5f1540d3aa9f4c01827b1bead628324c", "version_major": 2, "version_minor": 0 }, @@ -1409,6 +1512,153 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Yb2', pbc=True, cell=[17.36, 17.361, 17.362])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e4d6e53ee37943f1b75a4778faa31ebf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/699 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Lu2', pbc=True, cell=[16.988000000000003, 16.989000000000004, 16.990000000000002])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9014811f4ed94a0ab89bd85cbadb732f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/681 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Hf2', pbc=True, cell=[16.306, 16.307000000000002, 16.308])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "89586da069624d629410af7be1f1e473", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/657 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Ta2', pbc=True, cell=[15.686, 15.687, 15.688])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "537b2cf63c2940cda27b18a0396b7a84", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/631 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='W2', pbc=True, cell=[15.934, 15.934999999999999, 15.936])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e7c24191459544e4b2144c5241ae9d00", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/650 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Re2', pbc=True, cell=[15.438000000000002, 15.439000000000002, 15.440000000000003])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d82bb2fd3a2a46d3872d3b9d225c5986", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/636 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Atoms(symbols='Os2', pbc=True, cell=[15.376, 15.376999999999999, 15.378])\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d653e4c600854394b9e1793e2ad4aefb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/639 [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", @@ -1419,7 +1669,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3994e6dbc7eb42f4a4f0dabfe9bdadc4", + "model_id": "06ecb6dd44fc44d7bedcad9399b15622", "version_major": 2, "version_minor": 0 }, @@ -1440,7 +1690,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "091858598a094986a0e2cafeb05d4673", + "model_id": "14e45272ea124e6db825a235ab20485a", "version_major": 2, "version_minor": 0 }, @@ -1461,7 +1711,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "323e98ca8a914960b2e9eba184195bd1", + "model_id": "15b1e817990942b1887d1f9e69bf7c1e", "version_major": 2, "version_minor": 0 }, @@ -1482,7 +1732,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31170ef335cc47dbb5e69f7f3d8252a6", + "model_id": "bbccab2efd3e4cd1a10946a456de943a", "version_major": 2, "version_minor": 0 }, @@ -1503,7 +1753,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aada92001d55469888f69c338b8fdebf", + "model_id": "0592858c27ed4350ac931fd4fc5f58e1", "version_major": 2, "version_minor": 0 }, @@ -1524,7 +1774,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5dfd7154ebb2475a81c1eda1123aa0bf", + "model_id": "28249df131e24506a8f3ea28c9455176", "version_major": 2, "version_minor": 0 }, @@ -1545,7 +1795,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6f68da33e0c94d7abd0422ef273b887e", + "model_id": "52c77f90b3bd4e128bf111ee184e4669", "version_major": 2, "version_minor": 0 }, @@ -1566,7 +1816,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "db62430a8102448f8c57ea9a7569a1b6", + "model_id": "55d5fa3b5f9a43419f5a12381c0a27a1", "version_major": 2, "version_minor": 0 }, @@ -1587,7 +1837,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c14064f161c341dfba1a6cc23d808599", + "model_id": "09fbb07390fb4fbeaae4d557bd36e8f9", "version_major": 2, "version_minor": 0 }, @@ -1608,7 +1858,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "12291aa152424ab484a6721a8838347e", + "model_id": "65ce7e54b69a4a9191aa688a0817fe52", "version_major": 2, "version_minor": 0 }, @@ -1629,7 +1879,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0bd07aec4e874124b532e28efe4eaf5f", + "model_id": "e20691b9ed1945fb92739cbb373e5784", "version_major": 2, "version_minor": 0 }, @@ -1650,7 +1900,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8cec7d0d1bb44240b02cdd72d384371e", + "model_id": "e5ecefa32153447bb6cd6bc6e901b696", "version_major": 2, "version_minor": 0 }, @@ -1671,7 +1921,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38eefae6e06e4338ae75ff5093b20655", + "model_id": "5d63feb401bf4ce5a6d6b97f0b15a484", "version_major": 2, "version_minor": 0 }, @@ -1692,7 +1942,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "71290e296bf449e6bd2e532b12722cef", + "model_id": "c6b9b43a26dd43898e36af24e6a71bf1", "version_major": 2, "version_minor": 0 }, @@ -1704,283 +1954,16 @@ "output_type": "display_data" }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "../aten/src/ATen/native/cuda/Indexing.cu:1237: indexSelectSmallIndex: block: [0,0,0], thread: [96,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n", - 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"../aten/src/ATen/native/cuda/Indexing.cu:1237: indexSelectSmallIndex: block: [1,0,0], thread: [125,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n", - "../aten/src/ATen/native/cuda/Indexing.cu:1237: indexSelectSmallIndex: block: [1,0,0], thread: [126,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n", - "../aten/src/ATen/native/cuda/Indexing.cu:1237: indexSelectSmallIndex: block: [1,0,0], thread: [127,0,0] Assertion `srcIndex < srcSelectDimSize` failed.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", "Atoms(symbols='Pa2', pbc=True, cell=[17.855999999999998, 17.857, 17.857999999999997])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "de1bca44ed464ccd9bac3c05a4c2845b", + "model_id": "b3b5ebb1c83b45a0ba39a3e5a6791413", "version_major": 2, "version_minor": 0 }, @@ -1995,18 +1978,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", "Atoms(symbols='U2', pbc=True, cell=[16.802, 16.803, 16.804])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0b48eb17daa4f2992938d1a73cbc320", + "model_id": "38a83f18a3f24213a8eae042bcf12019", "version_major": 2, "version_minor": 0 }, @@ -2021,18 +1999,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", "Atoms(symbols='Np2', pbc=True, cell=[17.483999999999998, 17.485, 17.485999999999997])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a25fea3ea8ce4d22a6a66526d7d439ca", + "model_id": "2c08bcd50ca340c080e7527d571abdbe", "version_major": 2, "version_minor": 0 }, @@ -2047,18 +2020,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", "Atoms(symbols='Pu2', pbc=True, cell=[17.422, 17.423000000000002, 17.424])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b0e3f5f942541329f52ce669319104e", + "model_id": "79f1134294974bdeb837a5a011cc2d15", "version_major": 2, "version_minor": 0 }, @@ -2073,18 +2041,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", "Atoms(symbols='Am2', pbc=True, cell=[17.546, 17.547, 17.548])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa3342c116f843cf8e7087f149e09fef", + "model_id": "19e13233f2d34d8d9e89913acce2821d", "version_major": 2, "version_minor": 0 }, @@ -2099,18 +2062,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", "Atoms(symbols='Cm2', pbc=True, cell=[18.91, 18.911, 18.912])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7cf1e41dae09467cb438f27cc19dd4e8", + "model_id": "9896e5691f2247caabae48ae52d11ec9", "version_major": 2, "version_minor": 0 }, @@ -2125,18 +2083,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Bk2', pbc=True, cell=[21.08, 21.081, 21.081999999999997])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef86333810ec4f8880c7c7dab7d6c7ad", + "model_id": "c22f12fd957d45a591d3163481a523c2", "version_major": 2, "version_minor": 0 }, @@ -2151,18 +2105,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Cf2', pbc=True, cell=[18.91, 18.911, 18.912])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "07c430075db943df8e03f4ded0bd9d2f", + "model_id": "63b0a0d2d6494aba9446595dd9469856", "version_major": 2, "version_minor": 0 }, @@ -2177,18 +2127,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Es2', pbc=True, cell=[16.740000000000002, 16.741000000000003, 16.742])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b1c2b6167c7b4e369601458e2bbeac82", + "model_id": "17a7a93393464381917abc7e0ced997a", "version_major": 2, "version_minor": 0 }, @@ -2203,18 +2149,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Fm2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aaf00a4a650c4f90b7d183ac4d626ed6", + "model_id": "9d198ddf0f95482daa5bc29cc729e43f", "version_major": 2, "version_minor": 0 }, @@ -2229,18 +2171,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Md2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "04a689f74eec46ddb75675fab67aa9f5", + "model_id": "daf26fc9a40a4b4295f1a152d7f4d8b2", "version_major": 2, "version_minor": 0 }, @@ -2255,18 +2193,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='No2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f09c0c7eeab4b899ec41d54e4b403de", + "model_id": "0248fa704e4d4362bfb1a4c8e7130ecc", "version_major": 2, "version_minor": 0 }, @@ -2281,18 +2215,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Lr2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7fd49c209c43482f952d41c28363ec44", + "model_id": "6414629b0fb443fbb62aa8e0cc227f5f", "version_major": 2, "version_minor": 0 }, @@ -2307,18 +2237,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Rf2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c294b2793aaf4875b4af9792c8dba402", + "model_id": "5e60aa556cd64190a316eca2c46cbf93", "version_major": 2, "version_minor": 0 }, @@ -2333,18 +2259,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Db2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "beff0a1759a740cd850dbc0a9a65fea8", + "model_id": "ffb9dd11dad9417498e34b778d4ea807", "version_major": 2, "version_minor": 0 }, @@ -2359,18 +2281,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Sg2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "95b45f7f261b48edb9913800a68034a7", + "model_id": "c4f7b1d831cb484186fdd8e1d6e89eb9", "version_major": 2, "version_minor": 0 }, @@ -2385,18 +2303,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Bh2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "082ed6db90a7476aa84c249f2fa31a6d", + "model_id": "41ca932fa2bc4502bfc8801fd66b2d46", "version_major": 2, "version_minor": 0 }, @@ -2411,18 +2325,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Hs2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1bdf7d6eeeaf4b78a3a3d6383f4aab13", + "model_id": "0197657a588c46ae8397449bcadfd5df", "version_major": 2, "version_minor": 0 }, @@ -2437,18 +2347,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Mt2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7558626dbc342ccaf5b37541c8e1575", + "model_id": "3e6350e98a014e28a7da48321dfbc69b", "version_major": 2, "version_minor": 0 }, @@ -2463,18 +2369,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Ds2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac000438284a47f2b45e212225141349", + "model_id": "60915d35e2c7488b955a46e756226246", "version_major": 2, "version_minor": 0 }, @@ -2489,18 +2391,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Rg2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a7d6ab0bbd4149b1a27563d45cf7715d", + "model_id": "1c4548e494ae4b10acb2f0c8b6f2be90", "version_major": 2, "version_minor": 0 }, @@ -2515,18 +2413,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Cn2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "457140990a5f47ad9e7047cb6006056f", + "model_id": "2be77b0f982a468391f5f7ebfa8b75c0", "version_major": 2, "version_minor": 0 }, @@ -2541,18 +2435,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Nh2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9db17a0c4cb84a6d9d6bd8752d5d18c6", + "model_id": "9352ecb344694878893711eba254820d", "version_major": 2, "version_minor": 0 }, @@ -2567,18 +2457,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Fl2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bacec72e6c8c4dd2bb2d582180ca2f60", + "model_id": "3a79ff6d710145bc981294ab956dd2d0", "version_major": 2, "version_minor": 0 }, @@ -2593,18 +2479,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Mc2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d8b2f170eb334afab30b7409d3f8c58b", + "model_id": "8ecf3133cdc44afb9058aa06de7f2753", "version_major": 2, "version_minor": 0 }, @@ -2619,18 +2501,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Lv2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e3cbc309764417ab4b4585b6a5280d9", + "model_id": "af977980325744619ca1b8e752d0cd72", "version_major": 2, "version_minor": 0 }, @@ -2645,18 +2523,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Ts2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "280942de0d284c13bfb0e87f8865a5f9", + "model_id": "4ede4574968c418984449abde252c0ab", "version_major": 2, "version_minor": 0 }, @@ -2671,18 +2545,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n", + "Atomic number exceeds that given in model config\n", "Atoms(symbols='Og2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d255dfff643f481486ec79c4f149f763", + "model_id": "cefa8ab6ce3f449f8b882198315c971a", "version_major": 2, "version_minor": 0 }, @@ -2697,11 +2567,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA error: device-side assert triggered\n", - "CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\n", - "For debugging consider passing CUDA_LAUNCH_BLOCKING=1.\n", - "Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n", - "\n" + "Atomic number exceeds that given in model config\n" ] } ], @@ -2710,6 +2576,9 @@ " \n", " model_name = model.name\n", " \n", + " if model_name != 'eqV2(OMat)':\n", + " continue\n", + " \n", " print(f\"========== {model_name} ==========\")\n", "\n", " calc = MLIPEnum[model_name].value()\n", @@ -2802,7 +2671,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "a0ac2c09-370b-4fdd-bf74-ea5c4ade0215", "metadata": { "scrolled": true, @@ -2813,13 +2682,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "========== MACE-MP(M) ==========\n" + "========== eqV2(OMat) ==========\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "55562f662e56481ba346246ccc109626", + "model_id": "c23d83b4331c43e3bceb6d786f65d9ec", "version_major": 2, "version_minor": 0 }, @@ -2834,289 +2703,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_433641/3530606248.py:164: 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_433641/3530606248.py:156: RuntimeWarning: invalid value encountered in scalar divide\n", - " \"tortuosity\": etv / (abs(es[0] - es.min()) + (es[-1] - es.min())),\n", - "/tmp/ipykernel_433641/3530606248.py:158: 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_433641/3530606248.py:160: 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": "3a2235b0309e4dacab34cd8348b5963a", - "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_433641/3530606248.py:164: 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": "34c651d05a494eb2a0e7988adc9a9835", - "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_433641/3530606248.py:164: 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_433641/3530606248.py:159: 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": "7d0004a7880d4aeeb42532e4718d34e2", - "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_433641/3530606248.py:164: 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": "8e181eeb14f24447933b76dac47b9514", - "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_433641/3530606248.py:164: 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_433641/3530606248.py:161: 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": "0fda48c6de7b47ae93b62fde68263493", - "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_433641/3530606248.py:164: 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_433641/3530606248.py:156: RuntimeWarning: invalid value encountered in scalar divide\n", - " \"tortuosity\": etv / (abs(es[0] - es.min()) + (es[-1] - es.min())),\n", - "/tmp/ipykernel_433641/3530606248.py:160: 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_433641/3530606248.py:161: 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_433641/3530606248.py:156: 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": "f49debc4e85e4e2aa5c24de17cfa337a", - "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_433641/3530606248.py:164: 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": "55c068cb90a3463484ffd851c34a6ab8", - "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_433641/3530606248.py:164: 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": [ - "========== MACE-OFF(M) ==========\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f23884741dd74e2a9853298cc6c631b5", - "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_433641/3530606248.py:164: 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": "3da8dc9a994a4bd7b95d62413a73306f", - "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_433641/3530606248.py:164: 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_433641/3530606248.py:161: 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_1500708/3508918110.py:164: 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" ] } ], @@ -3300,7 +2888,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "id": "e0dd4367-3dca-440f-a7a9-7fdd84183f2c", "metadata": { "tags": [] @@ -3335,129 +2923,129 @@ "
118 rows × 19 columns
\n", + "95 rows × 19 columns
\n", "" ], "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", + " name method R \\\n", + "0 HH eqV2(OMat) [3.7199999999999998, 3.70996794, 3.69993586, 3... \n", + "1 HeHe eqV2(OMat) [4.433, 4.4229736200000005, 4.41294724, 4.4029... \n", + "2 LiLi eqV2(OMat) [6.572000000000001, 6.561981520000001, 6.55196... \n", + "3 BeBe eqV2(OMat) [6.138000000000001, 6.12797338, 6.117946759999... \n", + "4 BB eqV2(OMat) [5.921000000000001, 5.91097088, 5.900941739999... \n", + ".. ... ... ... \n", + "90 PaPa eqV2(OMat) [8.927999999999999, 8.91797468, 8.90794936, 8.... \n", + "91 UU eqV2(OMat) [8.401, 8.390974320000002, 8.38094864, 8.37092... \n", + "92 NpNp eqV2(OMat) [8.741999999999999, 8.7319829, 8.72196582, 8.7... \n", + "93 PuPu eqV2(OMat) [8.710999999999999, 8.70097432, 8.690948639999... \n", + "94 AmAm eqV2(OMat) [8.773, 8.7629818, 8.75296358, 8.7429453800000... \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", + " E \\\n", + "0 [-2.0984511375427246, -2.095881462097168, -2.0... \n", + "1 [0.5383987426757812, 0.5361838340759277, 0.536... \n", + "2 [-0.5126352310180664, -0.5116205215454102, -0.... \n", + "3 [0.23909759521484375, 0.2400522232055664, 0.23... \n", + "4 [-1.388545036315918, -1.3869714736938477, -1.3... \n", + ".. ... \n", + "90 [-8.536531448364258, -8.537920951843262, -8.55... \n", + "91 [-12.306029319763184, -12.309735298156738, -12... \n", + "92 [-16.419757843017578, -16.415634155273438, -16... \n", + "93 [-21.575969696044922, -21.568323135375977, -21... \n", + "94 [8.622910499572754, 8.642560958862305, 8.69469... \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", + " F S^2 force-flip-times \\\n", + "0 [9.72e-06, 7.52e-06, 3.211e-05, 3.564e-05, 3.9... [] 2 \n", + "1 [-1.365e-05, -4.945e-05, -8.538e-05, -0.000155... [] 2 \n", + "2 [-3.34e-05, 0.00016922, 0.00028867, 0.0005224,... [] 1 \n", + "3 [-4.817e-05, 0.00051783, 0.00090912, 0.0012907... [] 1 \n", + "4 [-7.486e-05, -0.00062429, -0.00124062, -0.0016... [] 1 \n", + ".. ... .. ... \n", + "90 [0.00149543, 0.00933722, 0.01594199, 0.0210447... [] 14 \n", + "91 [0.00250636, -0.00677688, -0.01450382, -0.0195... [] 9 \n", + "92 [0.00010047, 0.00343876, 0.00583172, 0.0066036... [] 13 \n", + "93 [0.00021465, -0.018026, -0.02833581, -0.022387... [] 25 \n", + "94 [-0.00028193, -0.00328918, -0.00321345, 0.0024... [] 8 \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", + " force-total-variation force-jump energy-diff-flip-times \\\n", + "0 106.606564 1.924082 17 \n", + "1 265.816823 5.186691 14 \n", + "2 28.116090 0.053238 31 \n", + "3 188.239996 0.177490 35 \n", + "4 142.788128 0.010691 19 \n", + ".. ... ... ... \n", + "90 293.319609 0.622614 47 \n", + "91 308.464112 1.041690 53 \n", + "92 366.700701 2.488891 58 \n", + "93 404.453349 20.662893 85 \n", + "94 63.571266 0.964731 58 \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", + " energy-grad-norm-max energy-jump energy-total-variation tortuosity \\\n", + "0 93.241588 0.564488 18.840863 1.555587 \n", + "1 77.250589 0.699341 19.451416 4.166659 \n", + "2 17.085735 0.181582 7.866485 1.334280 \n", + "3 22.672879 0.328797 13.735467 1.275790 \n", + "4 30.771105 0.572540 13.649937 1.145869 \n", + ".. ... ... ... ... \n", + "90 94.871632 1.276346 37.647346 3.460726 \n", + "91 74.002914 2.503158 36.593965 3.514434 \n", + "92 145.748637 3.159492 68.312851 6.846896 \n", + "93 195.927256 14.399994 78.172195 5.575810 \n", + "94 50.950555 2.227477 30.767544 2.925538 \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", + " conservation-deviation spearman-descending-force \\\n", + "0 2.842868 -0.994359 \n", + "1 10.740978 -0.973671 \n", + "2 1.063989 -0.998354 \n", + "3 6.498375 -0.997688 \n", + "4 4.867476 -1.000000 \n", + ".. ... ... \n", + "90 9.596265 -0.976682 \n", + "91 9.661410 -0.853855 \n", + "92 14.868774 -0.970815 \n", + "93 15.614091 -0.661178 \n", + "94 3.732556 -0.999440 \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", + " spearman-ascending-force spearman-repulsion-energy \\\n", + "0 -0.072700 -0.992857 \n", + "1 -0.546770 -0.912122 \n", + "2 0.976426 -0.981398 \n", + "3 0.983071 -0.972891 \n", + "4 0.990445 -0.984379 \n", + ".. ... ... \n", + "90 0.916942 -1.000000 \n", + "91 0.856809 -1.000000 \n", + "92 0.783776 -1.000000 \n", + "93 0.348212 -0.002303 \n", + "94 0.872640 -0.995198 \n", "\n", - " force-jump \n", - "118 458.210954 \n", - "119 1582.685197 \n", - "120 1721.766498 \n", - "121 1775.001526 \n", - "122 1799.183469 \n", - ".. ... \n", - "231 3926.252725 \n", - "232 3926.252725 \n", - "233 3926.252725 \n", - "234 3926.252725 \n", - "235 3926.252725 \n", + " spearman-attraction-energy \n", + "0 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