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"
@@ -2,20 +2,12 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "3200850a-b8fb-4f50-9815-16ae8da0f942",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Selected GPU cuda:0 with 40240.80 MB free memory from 1 GPUs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import os\n",
"from pathlib import Path\n",
@@ -31,71 +23,47 @@
"\n",
"from mlip_arena.models.utils import REGISTRY, MLIPEnum\n",
"\n",
- "model_name = \"ORB\"\n",
+ "# model_name = \"MACE-MP(M)\"\n",
"\n",
- "calc = MLIPEnum[model_name].value()"
+ "# calc = MLIPEnum[model_name].value()"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"id": "90887faa-1601-4c4c-9c44-d16731471d7f",
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "2a34049b06f74c64beb33d2e401fa32e",
- "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], calculator=SinglePointCalculator(...))\n"
+ "========== EquiformerV2(OC20) ==========\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "82a52529ce86432f95a5f463447066e6",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/356 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "Atoms(symbols='He2', pbc=True, cell=[8.866, 8.866999999999999, 8.868], calculator=SinglePointCalculator(...))\n"
+ "WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.\n",
+ "WARNING:root:Couldn't modify the submission pickle with error: [Errno 2] No such file or directory: '/checkpoint/abhshkdz/open-catalyst-project/logs/equiformer_v2/8307793/30669435_submitted.pkl'\n",
+ "WARNING:root:Skipping scheduler setup. No training set found.\n",
+ "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/fairchem/core/modules/normalization/normalizer.py:69: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+ " \"mean\": torch.tensor(state_dict[\"mean\"]),\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "de8eaef64c274ea9a8607c7fbe100600",
+ "model_id": "fb3a1b082e65432392452cd49c970a73",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/429 [00:00<?, ?it/s]"
+ " 0%| | 0/118 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -105,18 +73,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Li2', pbc=True, cell=[13.144000000000002, 13.145000000000001, 13.146000000000003], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='H2', pbc=True, cell=[7.4399999999999995, 7.441, 7.441999999999999])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d3b9908cae0a48a7b1bbf70ee1129f32",
+ "model_id": "ae877fcff60b488b9bf999c8e60ede7b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/593 [00:00<?, ?it/s]"
+ " 0%| | 0/344 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -126,18 +94,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Be2', pbc=True, cell=[12.276, 12.277, 12.278], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='He2', pbc=True, cell=[8.866, 8.866999999999999, 8.868])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "134edb09bf614ca384bad4f62341f253",
+ "model_id": "3129493feb1341f8a481440ca48c3022",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/565 [00:00<?, ?it/s]"
+ " 0%| | 0/418 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -147,18 +115,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='B2', pbc=True, cell=[11.842, 11.843, 11.844000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Li2', pbc=True, cell=[13.144000000000002, 13.145000000000001, 13.146000000000003])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "34ec8e0c4e0440869b2e9cc10a019940",
+ "model_id": "740fe86bac484a61b83a384823b1645b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
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+ " 0%| | 0/542 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -168,18 +136,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='C2', pbc=True, cell=[10.974, 10.975, 10.976], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Be2', pbc=True, cell=[12.276, 12.277, 12.278])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "023b7694e482409ba2049511c8fc9e9c",
+ "model_id": "f2649afe32f7486398ffd71c098b5481",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/510 [00:00<?, ?it/s]"
+ " 0%| | 0/527 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -189,18 +157,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='N2', pbc=True, cell=[10.292, 10.293, 10.294], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='B2', pbc=True, cell=[11.842, 11.843, 11.844000000000001])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f65690a50033416ca3bdd85087894165",
+ "model_id": "c7bbc608a47f413496fd445fb83dd747",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/479 [00:00<?, ?it/s]"
+ " 0%| | 0/516 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -210,18 +178,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='O2', pbc=True, cell=[9.3, 9.301, 9.302000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='C2', pbc=True, cell=[10.974, 10.975, 10.976])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "de08977d3ae4425e83498967cfa75a38",
+ "model_id": "d73537663862482798c4512e13763b16",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/432 [00:00<?, ?it/s]"
+ " 0%| | 0/480 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -231,18 +199,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='F2', pbc=True, cell=[9.052, 9.052999999999999, 9.054], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='N2', pbc=True, cell=[10.292, 10.293, 10.294])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "328152e659c246a4983ea98310ecbc78",
+ "model_id": "d26f8600f65f41a2a754ba489bd1fe64",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/450 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -252,18 +220,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ne2', pbc=True, cell=[9.796000000000001, 9.797, 9.798000000000002], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='O2', pbc=True, cell=[9.3, 9.301, 9.302000000000001])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ae4e8aae854f46a18d087f483f73a7c2",
+ "model_id": "2445be9f8d824f8a960a2380a50ed5ad",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/405 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -273,18 +241,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Na2', pbc=True, cell=[15.5, 15.501, 15.502], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='F2', pbc=True, cell=[9.052, 9.052999999999999, 9.054])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "08c7355cb19043dfbadcc35489aff0f9",
+ "model_id": "87d7c2beadb94a98a85736521676a92f",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/401 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -294,18 +262,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Mg2', pbc=True, cell=[15.562, 15.562999999999999, 15.564], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Ne2', pbc=True, cell=[9.796000000000001, 9.797, 9.798000000000002])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "ba8a4514d18b4168896ced34ecdc8afc",
+ "model_id": "c0b5603b4b7a4f798d28003cb8017e24",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/437 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -315,18 +283,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Al2', pbc=True, cell=[13.950000000000001, 13.951, 13.952000000000002], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Na2', pbc=True, cell=[15.5, 15.501, 15.502])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "60f622615590415a811160bec79d6a46",
+ "model_id": "717e21591149423f98c6ec8a57e2c007",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/625 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -336,18 +304,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Si2', pbc=True, cell=[13.578, 13.578999999999999, 13.58], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Mg2', pbc=True, cell=[15.562, 15.562999999999999, 15.564])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "8654d0c23c4f451eb723962d2e188c9e",
+ "model_id": "c48f33f121084bc2a47c96a26bb24d40",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/651 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -357,18 +325,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='P2', pbc=True, cell=[11.78, 11.780999999999999, 11.782], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Al2', pbc=True, cell=[13.950000000000001, 13.951, 13.952000000000002])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3fa40b18a2c74573968416cecc7ea826",
+ "model_id": "c49da4c1a00f4ef89ab53a8a58d8494c",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/588 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -378,18 +346,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='S2', pbc=True, cell=[11.718, 11.719, 11.72], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Si2', pbc=True, cell=[13.578, 13.578999999999999, 13.58])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3a3bfd6b13fc4543a01fa9794d5e82e1",
+ "model_id": "8062fddb9b7c4d5fbf7342785acc73bc",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/578 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -399,18 +367,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Cl2', pbc=True, cell=[11.284, 11.285, 11.286000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='P2', pbc=True, cell=[11.78, 11.780999999999999, 11.782])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "9e74d0f257dc4d3eb3700a3d012964fe",
+ "model_id": "7d320bd7c5484dfab71ace8529b70d1d",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/492 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -420,18 +388,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ar2', pbc=True, cell=[11.346, 11.347, 11.348], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='S2', pbc=True, cell=[11.718, 11.719, 11.72])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6a5081c2749d426cb53aeedcdf42c1a1",
+ "model_id": "6324d2c298a542828c07291c18c221cb",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/491 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -441,18 +409,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='K2', pbc=True, cell=[16.926000000000002, 16.927000000000003, 16.928], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Cl2', pbc=True, cell=[11.284, 11.285, 11.286000000000001])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7ba965cf95b64a2db36f9a18696b1328",
+ "model_id": "54c5575c22574894b4818ffdf81a76d5",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/472 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -462,18 +430,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ca2', pbc=True, cell=[16.244, 16.245, 16.246], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Ar2', pbc=True, cell=[11.346, 11.347, 11.348])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6ef0ca32999c4c12acc0f51200b0f463",
+ "model_id": "16ee4481621d4b2ab2814a926ec66961",
"version_major": 2,
"version_minor": 0
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+ " 0%| | 0/471 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -483,18 +451,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Sc2', pbc=True, cell=[15.996, 15.997, 15.998000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='K2', pbc=True, cell=[16.926000000000002, 16.927000000000003, 16.928])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7c8a9067bfde4538b94f60d1379e1c81",
+ "model_id": "40c58d9009e14342b5d63daffa92e500",
"version_major": 2,
"version_minor": 0
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+ " 0%| | 0/663 [00:00<?, ?it/s]"
]
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"metadata": {},
@@ -504,18 +472,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ti2', pbc=True, cell=[15.252, 15.253, 15.254000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Ca2', pbc=True, cell=[16.244, 16.245, 16.246])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c6a89e9e33334225a37e323430599fb1",
+ "model_id": "6c26cc1757ea4f1c92a4260d2eebaca9",
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"version_minor": 0
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"metadata": {},
@@ -525,18 +493,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='V2', pbc=True, cell=[15.004, 15.004999999999999, 15.006], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Sc2', pbc=True, cell=[15.996, 15.997, 15.998000000000001])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "b94b011139e2485b8edfdf8eae5b3a96",
+ "model_id": "1a8a3149ff4f4c7797b366cab2419e19",
"version_major": 2,
"version_minor": 0
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},
"metadata": {},
@@ -546,18 +514,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Cr2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Ti2', pbc=True, cell=[15.252, 15.253, 15.254000000000001])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a34a7474b2424d6da2415eedc4eb5ed5",
+ "model_id": "e17caa1e90444469a529c782f74c6f44",
"version_major": 2,
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]
},
"metadata": {},
@@ -567,18 +535,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Mn2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='V2', pbc=True, cell=[15.004, 15.004999999999999, 15.006])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3a1385681aa34d50830e1786b375dc1b",
+ "model_id": "80181fe3673a4f9c81237113fb09241a",
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"version_minor": 0
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+ " 0%| | 0/612 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -588,18 +556,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Fe2', pbc=True, cell=[15.128, 15.129, 15.13], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Cr2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "96a14120762b41edbbe5fa2801465c00",
+ "model_id": "cec3efec611048898f3c253919e77c9c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/690 [00:00<?, ?it/s]"
+ " 0%| | 0/634 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -609,18 +577,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Co2', pbc=True, cell=[14.879999999999999, 14.880999999999998, 14.882], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Mn2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "50c01502939f4a089e240a32a19e4715",
+ "model_id": "0cbe0177d37145a09368da269439e9d3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/681 [00:00<?, ?it/s]"
+ " 0%| | 0/634 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -630,18 +598,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ni2', pbc=True, cell=[14.879999999999999, 14.880999999999998, 14.882], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Fe2', pbc=True, cell=[15.128, 15.129, 15.13])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "8318f5bea5b34eebbe3fa62d46a6ddd3",
+ "model_id": "0224bdbfba704f7d8e7bbab53038785d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/681 [00:00<?, ?it/s]"
+ " 0%| | 0/637 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -651,18 +619,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Cu2', pbc=True, cell=[14.756, 14.757, 14.758000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Co2', pbc=True, cell=[14.879999999999999, 14.880999999999998, 14.882])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7b4fb01265f6484f8e5da57da648cad6",
+ "model_id": "c9e09cee05b749e3b4be596a46d11584",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/671 [00:00<?, ?it/s]"
+ " 0%| | 0/630 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -672,18 +640,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Zn2', pbc=True, cell=[14.818000000000001, 14.819, 14.820000000000002], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Ni2', pbc=True, cell=[14.879999999999999, 14.880999999999998, 14.882])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d5466219b42644beaa44933e080d97fd",
+ "model_id": "857608f7605140ecb55488b3ffbc0dc7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/679 [00:00<?, ?it/s]"
+ " 0%| | 0/632 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -693,18 +661,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ga2', pbc=True, cell=[14.383999999999999, 14.384999999999998, 14.386], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Cu2', pbc=True, cell=[14.756, 14.757, 14.758000000000001])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "76173ffef24649ca9cb3db75d342bf39",
+ "model_id": "1d5ed5324d4c401ca9479f06587a593b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/658 [00:00<?, ?it/s]"
+ " 0%| | 0/618 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -714,18 +682,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ge2', pbc=True, cell=[14.198, 14.199, 14.200000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Zn2', pbc=True, cell=[14.818000000000001, 14.819, 14.820000000000002])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d23e93b476eb423992f0622482c31028",
+ "model_id": "d89a24804f30470994a9777666d7a055",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/649 [00:00<?, ?it/s]"
+ " 0%| | 0/631 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -735,18 +703,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='As2', pbc=True, cell=[11.655999999999999, 11.656999999999998, 11.658], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Ga2', pbc=True, cell=[14.383999999999999, 14.384999999999998, 14.386])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "71e6eba6689441159c1143cd674822b3",
+ "model_id": "9b88430fbc464a5fa1114cb251948b94",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/523 [00:00<?, ?it/s]"
+ " 0%| | 0/609 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -756,18 +724,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Se2', pbc=True, cell=[11.284, 11.285, 11.286000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Ge2', pbc=True, cell=[14.198, 14.199, 14.200000000000001])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d2a6fe20ec5b4031a34d4f7ba890facd",
+ "model_id": "169860b84069488380ae557fb7cf5ed1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/504 [00:00<?, ?it/s]"
+ " 0%| | 0/601 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -777,18 +745,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Br2', pbc=True, cell=[11.532000000000002, 11.533000000000001, 11.534000000000002], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='As2', pbc=True, cell=[11.655999999999999, 11.656999999999998, 11.658])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "af11139f3eaa4484a53bee33ea7a2d35",
+ "model_id": "0a76df7eccb14dea84019b81b0fd0b3e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/516 [00:00<?, ?it/s]"
+ " 0%| | 0/475 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -798,18 +766,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Kr2', pbc=True, cell=[13.950000000000001, 13.951, 13.952000000000002], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Se2', pbc=True, cell=[11.284, 11.285, 11.286000000000001])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d5b8a887e947405bb2a62f9e6b2bb73c",
+ "model_id": "a7187637bd0449baa9ba01567467692f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/639 [00:00<?, ?it/s]"
+ " 0%| | 0/456 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -819,18 +787,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Rb2', pbc=True, cell=[19.902, 19.903000000000002, 19.904], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Br2', pbc=True, cell=[11.532000000000002, 11.533000000000001, 11.534000000000002])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "eedd87ebf0f842a7ad71c077be48d83a",
+ "model_id": "08eecc5ce1da4e4a852a5439bc2d7845",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/895 [00:00<?, ?it/s]"
+ " 0%| | 0/468 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -840,18 +808,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Sr2', pbc=True, cell=[17.608, 17.609, 17.61], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Kr2', pbc=True, cell=[13.950000000000001, 13.951, 13.952000000000002])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7d1c3e21586343279bbad3b96c8440d0",
+ "model_id": "bd3e4d3b58e14a9783ee394a15e1aca1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/782 [00:00<?, ?it/s]"
+ " 0%| | 0/593 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -861,18 +829,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Y2', pbc=True, cell=[17.05, 17.051000000000002, 17.052], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Rb2', pbc=True, cell=[19.902, 19.903000000000002, 19.904])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "2d24c0b823ba4e2e8192dbd27cc9b94c",
+ "model_id": "d304b8fb931645d8b7f0ff5e31fcb21f",
"version_major": 2,
"version_minor": 0
},
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- " 0%| | 0/757 [00:00<?, ?it/s]"
+ " 0%| | 0/797 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -882,18 +850,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Zr2', pbc=True, cell=[15.624, 15.625, 15.626000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Sr2', pbc=True, cell=[17.608, 17.609, 17.61])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4a2829b006444ac8a1e75f6ace407e24",
+ "model_id": "48b5a4ab0c6c46eea0623f3b419935ed",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/693 [00:00<?, ?it/s]"
+ " 0%| | 0/704 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -903,18 +871,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Nb2', pbc=True, cell=[15.872000000000002, 15.873000000000001, 15.874000000000002], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Y2', pbc=True, cell=[17.05, 17.051000000000002, 17.052])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "891b7f1a99ae46efb7b197409abf5a1a",
+ "model_id": "5e95b61650f6494fbd300072e7f8ad60",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
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+ " 0%| | 0/681 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -924,18 +892,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Mo2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Zr2', pbc=True, cell=[15.624, 15.625, 15.626000000000001])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "7f91c52d6a5d47c99bea0e6ff2fc3b07",
+ "model_id": "1abe8f3089954547af62d929e97d55fa",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/623 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -945,18 +913,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Tc2', pbc=True, cell=[15.128, 15.129, 15.13], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Nb2', pbc=True, cell=[15.872000000000002, 15.873000000000001, 15.874000000000002])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c8725491bc944e128ef800c09fdb45f9",
+ "model_id": "94f02034d0cd465181de8b39084dad42",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/646 [00:00<?, ?it/s]"
]
},
"metadata": {},
@@ -966,291 +934,337 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ru2', pbc=True, cell=[15.252, 15.253, 15.254000000000001], calculator=SinglePointCalculator(...))\n"
+ "Atoms(symbols='Mo2', pbc=True, cell=[15.190000000000001, 15.191, 15.192000000000002])\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4a622084126e4e26957372e5b3915a21",
+ "model_id": "2210fd8038384aa6888b45285c474e47",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/689 [00:00<?, ?it/s]"
+ " 0%| | 0/620 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
- },
+ }
+ ],
+ "source": [
+ "for model in MLIPEnum:\n",
+ " \n",
+ " model_name = model.name\n",
+ " \n",
+ " if model_name == 'EquiformerV2(OC22)':\n",
+ " continue\n",
+ " \n",
+ " json_fpath = Path(REGISTRY[model_name][\"family\"]) / \"homonuclear-diatomics.json\"\n",
+ " \n",
+ " if json_fpath.exists():\n",
+ " continue\n",
+ " \n",
+ " print(f\"========== {model_name} ==========\")\n",
+ "\n",
+ " calc = MLIPEnum[model_name].value()\n",
+ "\n",
+ " for symbol in tqdm(chemical_symbols[1:]):\n",
+ "\n",
+ " s = set([symbol])\n",
+ "\n",
+ " if \"X\" in s:\n",
+ " continue\n",
+ "\n",
+ " try:\n",
+ " atom = Atom(symbol)\n",
+ " rmin = 0.9 * covalent_radii[atom.number]\n",
+ " rvdw = vdw_alvarez.vdw_radii[atom.number] if atom.number < len(vdw_alvarez.vdw_radii) else np.nan\n",
+ " rmax = 3.1 * rvdw if not np.isnan(rvdw) else 6\n",
+ " rstep = 0.01\n",
+ "\n",
+ " a = 2 * rmax\n",
+ "\n",
+ " npts = int((rmax - rmin)/rstep)\n",
+ "\n",
+ " rs = np.linspace(rmin, rmax, npts)\n",
+ " es = np.zeros_like(rs)\n",
+ "\n",
+ " da = symbol + symbol\n",
+ "\n",
+ " out_dir = Path(REGISTRY[model_name][\"family\"]) / str(da)\n",
+ " os.makedirs(out_dir, exist_ok=True)\n",
+ "\n",
+ " skip = 0\n",
+ "\n",
+ " element = Element(symbol)\n",
+ "\n",
+ " try:\n",
+ " m = element.valence[1]\n",
+ " if element.valence == (0, 2):\n",
+ " m = 0\n",
+ " except:\n",
+ " m = 0\n",
+ "\n",
+ "\n",
+ " r = rs[0]\n",
+ "\n",
+ " positions = [\n",
+ " [a/2-r/2, a/2, a/2],\n",
+ " [a/2+r/2, a/2, a/2],\n",
+ " ]\n",
+ "\n",
+ " traj_fpath = out_dir / f\"{model_name}.extxyz\"\n",
+ "\n",
+ " if traj_fpath.exists():\n",
+ " traj = read(traj_fpath, index=\":\")\n",
+ " skip = len(traj)\n",
+ " atoms = traj[-1]\n",
+ " else:\n",
+ " # Create the unit cell with two atoms\n",
+ " atoms = Atoms(\n",
+ " da,\n",
+ " positions=positions,\n",
+ " # magmoms=magmoms,\n",
+ " cell=[a, a+0.001, a+0.002],\n",
+ " pbc=True\n",
+ " )\n",
+ "\n",
+ " print(atoms)\n",
+ "\n",
+ " atoms.calc = calc\n",
+ "\n",
+ " for i, r in enumerate(tqdm(rs)):\n",
+ "\n",
+ " if i < skip:\n",
+ " continue\n",
+ "\n",
+ " positions = [\n",
+ " [a/2-r/2, a/2, a/2],\n",
+ " [a/2+r/2, a/2, a/2],\n",
+ " ]\n",
+ "\n",
+ " # atoms.set_initial_magnetic_moments(magmoms)\n",
+ "\n",
+ " atoms.set_positions(positions)\n",
+ "\n",
+ " es[i] = atoms.get_potential_energy()\n",
+ "\n",
+ " write(traj_fpath, atoms, append=\"a\")\n",
+ " except Exception as e:\n",
+ " print(e)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "a0ac2c09-370b-4fdd-bf74-ea5c4ade0215",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Rh2', pbc=True, cell=[15.128, 15.129, 15.13], calculator=SinglePointCalculator(...))\n"
+ "========== MACE-MP(M) ==========\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e0fc5917c77142db9ffa1d028442e0d4",
+ "model_id": "597310b16837405fa6674583f7ea6892",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/685 [00:00<?, ?it/s]"
+ " 0%| | 0/118 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "Atoms(symbols='Pd2', pbc=True, cell=[13.33, 13.331, 13.332], calculator=SinglePointCalculator(...))\n"
+ "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+ " df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+ "/tmp/ipykernel_768993/102531167.py:101: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+ " \"spearman-descending-force\": stats.spearmanr(rs[iminf:], fs[iminf:]).statistic,\n",
+ "/tmp/ipykernel_768993/102531167.py:103: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+ " \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n"
]
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "7b4b74b817aa4acf9795a23df0a91cd4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/597 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ag2', pbc=True, cell=[15.686, 15.687, 15.688], calculator=SinglePointCalculator(...))\n"
+ "========== CHGNet ==========\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "48b1a2a0c7174dfa8e00e77d1a96ca72",
+ "model_id": "7e33d5e27fc144dfa24b28307d003e18",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- " 0%| | 0/711 [00:00<?, ?it/s]"
+ " 0%| | 0/118 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "Atoms(symbols='Cd2', pbc=True, cell=[15.438000000000002, 15.439000000000002, 15.440000000000003], calculator=SinglePointCalculator(...))\n"
+ "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+ " df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n"
]
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "2b186742556b41cdb6b7948ea28a290f",
- "version_major": 2,
- "version_minor": 0
- },
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- " 0%| | 0/699 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='In2', pbc=True, cell=[15.066, 15.067, 15.068000000000001], calculator=SinglePointCalculator(...))\n"
+ "========== M3GNet ==========\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "65d7f43323ed4f98855be84066f0fe09",
+ "model_id": "9e2521f6b5314c65adadb27928af771b",
"version_major": 2,
"version_minor": 0
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},
"metadata": {},
"output_type": "display_data"
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "Atoms(symbols='Sn2', pbc=True, cell=[15.004, 15.004999999999999, 15.006], calculator=SinglePointCalculator(...))\n"
+ "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+ " df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+ "/tmp/ipykernel_768993/102531167.py:102: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+ " \"spearman-ascending-force\": stats.spearmanr(rs[:iminf], fs[:iminf]).statistic,\n"
]
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9cbb5d7d967e4419816693ec9b4ece4c",
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Sb2', pbc=True, cell=[15.314000000000002, 15.315000000000001, 15.316000000000003], calculator=SinglePointCalculator(...))\n"
+ "========== ORB ==========\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "5ae79add3902404fa215a6af7a2525df",
+ "model_id": "dc4eb14a89484a89a0f3efceddb1101a",
"version_major": 2,
"version_minor": 0
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},
"metadata": {},
"output_type": "display_data"
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "Atoms(symbols='Te2', pbc=True, cell=[12.338000000000001, 12.339, 12.340000000000002], calculator=SinglePointCalculator(...))\n"
+ "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+ " df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n"
]
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "0dde4914cef24d40b375684cc2530dbe",
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='I2', pbc=True, cell=[12.648000000000001, 12.649000000000001, 12.650000000000002], calculator=SinglePointCalculator(...))\n"
+ "========== SevenNet ==========\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a8fc22c9ac874ea8bba2762e015227e1",
+ "model_id": "0701d73663f5483ba64f000208e73b97",
"version_major": 2,
"version_minor": 0
},
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},
"metadata": {},
"output_type": "display_data"
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "Atoms(symbols='Xe2', pbc=True, cell=[12.772, 12.773, 12.774000000000001], calculator=SinglePointCalculator(...))\n"
+ "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+ " df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+ "/tmp/ipykernel_768993/102531167.py:104: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+ " \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n"
]
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "c1a5cdf4a14e407380b0370c2b7ccece",
- "version_major": 2,
- "version_minor": 0
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Cs2', pbc=True, cell=[21.576, 21.577, 21.578], calculator=SinglePointCalculator(...))\n"
+ "========== EquiformerV2(OC22) ==========\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "50310e69a76a42629828e76a6198f01a",
+ "model_id": "4b773e96037947d9b51f710e50b3635b",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/118 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ba2', pbc=True, cell=[18.785999999999998, 18.787, 18.787999999999997], calculator=SinglePointCalculator(...))\n"
+ "/tmp/ipykernel_768993/102531167.py:107: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+ " df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+ "/tmp/ipykernel_768993/102531167.py:103: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+ " \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n",
+ "/tmp/ipykernel_768993/102531167.py:104: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n",
+ " \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n"
]
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "72dac368f194409a8595141ae1a4b0da",
- "version_major": 2,
- "version_minor": 0
- },
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- " 0%| | 0/839 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='La2', pbc=True, cell=[18.476, 18.477, 18.477999999999998], calculator=SinglePointCalculator(...))\n"
+ "========== EquiformerV2(OC20) ==========\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f216bf02086744428c860dece5094292",
+ "model_id": "f5f6c8438a9c4a3892d6b3cbf94f1910",
"version_major": 2,
"version_minor": 0
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]
},
"metadata": {},
@@ -1260,39 +1274,26 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Ce2', pbc=True, cell=[17.855999999999998, 17.857, 17.857999999999997], calculator=SinglePointCalculator(...))\n"
+ "========== eSCN(OC20) ==========\n"
]
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "f25443a1c9d944e0adbd74a1d3af4bc5",
- "version_major": 2,
- "version_minor": 0
- },
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- " 0%| | 0/792 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "Atoms(symbols='Pr2', pbc=True, cell=[18.104, 18.105, 18.105999999999998], calculator=SinglePointCalculator(...))\n"
+ "/tmp/ipykernel_768993/102531167.py:114: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+ " df = pd.concat([df0, df], ignore_index=True)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3b1be7ef41de4d6e91aba2147d03ee6e",
+ "model_id": "4f3e200efba948aea10d1d937cd787fd",
"version_major": 2,
"version_minor": 0
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},
"metadata": {},
@@ -1302,18 +1303,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Nd2', pbc=True, cell=[18.290000000000003, 18.291000000000004, 18.292], calculator=SinglePointCalculator(...))\n"
+ "========== MACE-OFF(M) ==========\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "47f8e5fe8b3b4427822e63abf30381e6",
+ "model_id": "e51884511ccc43338c5e621b7b6c35be",
"version_major": 2,
"version_minor": 0
},
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]
},
"metadata": {},
@@ -1323,4501 +1324,587 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Atoms(symbols='Pm2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
+ "========== ALIGNN ==========\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "c0a056aeb8b84be1873827b83c1514d2",
+ "model_id": "85868bdd44274852b7e8f414c36e2a8a",
"version_major": 2,
"version_minor": 0
},
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+ " 0%| | 0/118 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Sm2', pbc=True, cell=[17.98, 17.981, 17.982], calculator=SinglePointCalculator(...))\n"
+ "ename": "OSError",
+ "evalue": "Cannot save file into a non-existent directory: 'alignn'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[3], line 117\u001b[0m\n\u001b[1;32m 114\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([df0, df], ignore_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 115\u001b[0m df\u001b[38;5;241m.\u001b[39mdrop_duplicates(inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, subset\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmethod\u001b[39m\u001b[38;5;124m\"\u001b[39m], keep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlast\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 117\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_json\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson_fpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrecords\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/util/_decorators.py:333\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 328\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 329\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m 330\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 331\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m 332\u001b[0m )\n\u001b[0;32m--> 333\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/core/generic.py:2702\u001b[0m, in \u001b[0;36mNDFrame.to_json\u001b[0;34m(self, path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options, mode)\u001b[0m\n\u001b[1;32m 2699\u001b[0m config\u001b[38;5;241m.\u001b[39mis_nonnegative_int(indent)\n\u001b[1;32m 2700\u001b[0m indent \u001b[38;5;241m=\u001b[39m indent \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m-> 2702\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_json\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2703\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2704\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2705\u001b[0m \u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2706\u001b[0m \u001b[43m \u001b[49m\u001b[43mdate_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2707\u001b[0m \u001b[43m \u001b[49m\u001b[43mdouble_precision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdouble_precision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2708\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_ascii\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_ascii\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2709\u001b[0m \u001b[43m \u001b[49m\u001b[43mdate_unit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_unit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2710\u001b[0m \u001b[43m \u001b[49m\u001b[43mdefault_handler\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_handler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2711\u001b[0m \u001b[43m \u001b[49m\u001b[43mlines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlines\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2712\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2713\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2714\u001b[0m \u001b[43m \u001b[49m\u001b[43mindent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2715\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2716\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2717\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/io/json/_json.py:217\u001b[0m, in \u001b[0;36mto_json\u001b[0;34m(path_or_buf, obj, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options, mode)\u001b[0m\n\u001b[1;32m 213\u001b[0m s \u001b[38;5;241m=\u001b[39m convert_to_line_delimits(s)\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m path_or_buf \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 216\u001b[0m \u001b[38;5;66;03m# apply compression and byte/text conversion\u001b[39;00m\n\u001b[0;32m--> 217\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 218\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\n\u001b[1;32m 219\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m handles:\n\u001b[1;32m 220\u001b[0m handles\u001b[38;5;241m.\u001b[39mhandle\u001b[38;5;241m.\u001b[39mwrite(s)\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+ "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/io/common.py:749\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;66;03m# Only for write methods\u001b[39;00m\n\u001b[1;32m 748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode \u001b[38;5;129;01mand\u001b[39;00m is_path:\n\u001b[0;32m--> 749\u001b[0m \u001b[43mcheck_parent_directory\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression:\n\u001b[1;32m 752\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzstd\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 753\u001b[0m \u001b[38;5;66;03m# compression libraries do not like an explicit text-mode\u001b[39;00m\n",
+ "File \u001b[0;32m/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pandas/io/common.py:616\u001b[0m, in \u001b[0;36mcheck_parent_directory\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 614\u001b[0m parent \u001b[38;5;241m=\u001b[39m Path(path)\u001b[38;5;241m.\u001b[39mparent\n\u001b[1;32m 615\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m parent\u001b[38;5;241m.\u001b[39mis_dir():\n\u001b[0;32m--> 616\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124mrf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot save file into a non-existent directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparent\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+ "\u001b[0;31mOSError\u001b[0m: Cannot save file into a non-existent directory: 'alignn'"
]
- },
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "for model in MLIPEnum:\n",
+ " \n",
+ " model_name = model.name\n",
+ " \n",
+ " print(f\"========== {model_name} ==========\")\n",
+ " \n",
+ " df = pd.DataFrame(columns=[\n",
+ " \"name\", \n",
+ " \"method\", \n",
+ " \"R\", \"E\", \"F\", \"S^2\", \n",
+ " \"force-flip-times\",\n",
+ " \"force-total-variation\",\n",
+ " \"energy-diff-flip-times\",\n",
+ " \"energy-grad-norm-max\",\n",
+ " \"energy-jump\",\n",
+ " \"energy-total-variation\",\n",
+ " \"conservation-deviation\",\n",
+ " \"spearman-descending-force\",\n",
+ " \"spearman-ascending-force\",\n",
+ " \"spearman-repulsion-energy\",\n",
+ " \"spearman-attraction-energy\"\n",
+ " ])\n",
+ " \n",
+ "\n",
+ " for symbol in tqdm(chemical_symbols[1:]):\n",
+ "\n",
+ " da = symbol + symbol\n",
+ "\n",
+ " out_dir = Path(REGISTRY[model_name][\"family\"]) / da\n",
+ "\n",
+ " traj_fpath = out_dir / f\"{model_name}.extxyz\"\n",
+ "\n",
+ "\n",
+ " if traj_fpath.exists():\n",
+ " traj = read(traj_fpath, index=\":\")\n",
+ " else:\n",
+ " continue\n",
+ "\n",
+ " Rs, Es, Fs, S2s = [], [], [], []\n",
+ " for atoms in traj:\n",
+ "\n",
+ " vec = atoms.positions[1] - atoms.positions[0]\n",
+ " r = np.linalg.norm(vec)\n",
+ " e = atoms.get_potential_energy()\n",
+ " f = np.inner(vec/r, atoms.get_forces()[1])\n",
+ " # s2 = np.mean(np.power(atoms.get_magnetic_moments(), 2))\n",
+ "\n",
+ " Rs.append(r)\n",
+ " Es.append(e)\n",
+ " Fs.append(f)\n",
+ " # S2s.append(s2)\n",
+ "\n",
+ " rs = np.array(Rs)\n",
+ " es = np.array(Es)\n",
+ " fs = np.array(Fs)\n",
+ "\n",
+ " indices = np.argsort(rs)[::-1]\n",
+ " rs = rs[indices]\n",
+ " es = es[indices]\n",
+ " fs = fs[indices]\n",
+ "\n",
+ " iminf = np.argmin(fs)\n",
+ " imine = np.argmin(es)\n",
+ "\n",
+ " de_dr = np.gradient(es, rs)\n",
+ " d2e_dr2 = np.gradient(de_dr, rs)\n",
+ "\n",
+ " rounded_fs = np.copy(fs)\n",
+ " rounded_fs[np.abs(rounded_fs) < 1e-2] = 0\n",
+ " fs_sign = np.sign(rounded_fs)\n",
+ " fs_sign = fs_sign[fs_sign != 0]\n",
+ "\n",
+ " # rounded_ediff = np.diff(es)\n",
+ " # rounded_ediff[np.abs(rounded_ediff) < zero_threshold] = 0\n",
+ " ediff = np.diff(es)\n",
+ " ediff[np.abs(ediff) < 1e-3] = 0\n",
+ " ediff_sign = np.sign(ediff)\n",
+ " mask = ediff_sign != 0\n",
+ " ediff = ediff[mask]\n",
+ " ediff_sign = ediff_sign[mask]\n",
+ " ediff_flip = np.diff(ediff_sign) != 0\n",
+ " ejump = np.abs(ediff[:-1][ediff_flip]).sum() + np.abs(ediff[1:][ediff_flip]).sum()\n",
+ "\n",
+ " conservation_deviation = np.mean(np.abs(fs + de_dr))\n",
+ "\n",
+ " data = {\n",
+ " \"name\": da,\n",
+ " \"method\": model_name,\n",
+ " \"R\": rs,\n",
+ " \"E\": es,\n",
+ " \"F\": fs,\n",
+ " \"S^2\": S2s,\n",
+ " \"force-flip-times\": np.sum(np.diff(fs_sign)!=0),\n",
+ " \"force-total-variation\": np.sum(np.abs(np.diff(fs))),\n",
+ " \"energy-diff-flip-times\": np.sum(ediff_flip),\n",
+ " \"energy-grad-norm-max\": np.max(np.abs(de_dr)),\n",
+ " \"energy-jump\": ejump,\n",
+ " # \"energy-grad-norm-mean\": np.mean(de_dr_abs),\n",
+ " \"energy-total-variation\": np.sum(np.abs(np.diff(es))),\n",
+ " \"conservation-deviation\": conservation_deviation,\n",
+ " \"spearman-descending-force\": stats.spearmanr(rs[iminf:], fs[iminf:]).statistic,\n",
+ " \"spearman-ascending-force\": stats.spearmanr(rs[:iminf], fs[:iminf]).statistic,\n",
+ " \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n",
+ " \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n",
+ " }\n",
+ "\n",
+ " df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
+ "\n",
+ "\n",
+ " json_fpath = Path(REGISTRY[model_name][\"family\"]) / \"homonuclear-diatomics.json\"\n",
+ "\n",
+ " if json_fpath.exists():\n",
+ " df0 = pd.read_json(json_fpath)\n",
+ " df = pd.concat([df0, df], ignore_index=True)\n",
+ " df.drop_duplicates(inplace=True, subset=[\"name\", \"method\"], keep='last')\n",
+ "\n",
+ " df.to_json(json_fpath, orient=\"records\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "e0dd4367-3dca-440f-a7a9-7fdd84183f2c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
{
"data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "f26af1f9df7946ff86eef37f445660ad",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/800 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Eu2', pbc=True, cell=[17.794, 17.795, 17.796], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "070005a27b7e4d9fb2fc838d08d26575",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/790 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Gd2', pbc=True, cell=[17.546, 17.547, 17.548], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "ef0047d15224430c971153c153b99a2f",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/779 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Tb2', pbc=True, cell=[17.298000000000002, 17.299000000000003, 17.3], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1bcbffde6a8e429daef279876b528c15",
- "version_major": 2,
- "version_minor": 0
- },
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- " 0%| | 0/767 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Dy2', pbc=True, cell=[17.794, 17.795, 17.796], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "baa9e8204ab143f787b3db78b268b198",
- "version_major": 2,
- "version_minor": 0
- },
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- " 0%| | 0/793 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Ho2', pbc=True, cell=[17.422, 17.423000000000002, 17.424], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "aeaaaa130724471e84166b101554192b",
- "version_major": 2,
- "version_minor": 0
- },
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- " 0%| | 0/775 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Er2', pbc=True, cell=[17.546, 17.547, 17.548], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "cec839ccce844caf88528ae86ba2988d",
- "version_major": 2,
- "version_minor": 0
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Tm2', pbc=True, cell=[17.298000000000002, 17.299000000000003, 17.3], calculator=SinglePointCalculator(...))\n"
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- "metadata": {},
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- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Db2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
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- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Sg2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
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- "metadata": {},
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- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Bh2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
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- "metadata": {},
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- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Hs2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
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- "metadata": {},
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- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Mt2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
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- "metadata": {},
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- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Ds2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
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- "metadata": {},
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- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Rg2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
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- "metadata": {},
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- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Cn2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
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- ]
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- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Nh2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Fl2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Mc2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Lv2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Ts2', pbc=True, cell=[12.0, 12.001, 12.002], calculator=SinglePointCalculator(...))\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
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- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Atoms(symbols='Og2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1ecca992966c450ba52ab1484fce4f45",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/590 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Class values must be smaller than num_classes.\n"
- ]
- }
- ],
- "source": [
- "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 = min(covalent_radii[atom.number] * 0.5, 1.0)\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 #if rmin < 1 else 0.4\n",
- "\n",
- " a = 2 * rmax\n",
- "\n",
- " npts = int((rmax - rmin)/rstep)\n",
- "\n",
- " rs = np.linspace(rmin, rmax, npts)\n",
- " e = np.zeros_like(rs)\n",
- "\n",
- " da = symbol + symbol\n",
- "\n",
- " out_dir = Path(REGISTRY[model_name][\"family\"]) / str(da)\n",
- "\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",
- " e[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": 16,
- "id": "a0ac2c09-370b-4fdd-bf74-ea5c4ade0215",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6adfb4c128c0413b8f70054e3241979d",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/118 [00:00<?, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "\n",
- "\n",
- "df = pd.DataFrame(columns=[\"name\", \"method\", \"R\", \"E\", \"F\", \"S^2\", \"spearman-repulsion\", \"spearman-attraction\"])\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",
- " data = {\n",
- " \"name\": da,\n",
- " \"method\": model_name,\n",
- " \"R\": Rs,\n",
- " \"E\": Es,\n",
- " \"F\": Fs,\n",
- " \"S^2\": S2s,\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\"])\n",
- "\n",
- "df.to_json(json_fpath, orient=\"records\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "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>spearman-repulsion</th>\n",
- " <th>spearman-attraction</th>\n",
- " <th>spearman-descending-force</th>\n",
- " <th>spearman-ascending-force</th>\n",
- " <th>spearman-repulsion-energy</th>\n",
- " <th>spearman-attraction-energy</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>HH</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.1549999999999998, 0.16504226000000033, 0.17...</td>\n",
- " <td>[-2.6258459091186523, -2.755453109741211, -1.8...</td>\n",
- " <td>[0.26894104, -0.0038066, 2.37577677, 7.8109564...</td>\n",
- " <td>[]</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>-0.908097</td>\n",
- " <td>0.579246</td>\n",
- " <td>-0.858528</td>\n",
- " <td>0.504446</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>HeHe</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.13999999999999968, 0.15003038000000046, 0.1...</td>\n",
- " <td>[-0.37155914306640625, -0.4051551818847656, 0....</td>\n",
- " <td>[0.02356668, -2.673e-05, 0.45423254, 1.2784304...</td>\n",
- " <td>[]</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>-0.911505</td>\n",
- " <td>-0.046725</td>\n",
- " <td>-0.866669</td>\n",
- " <td>0.340055</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>LiLi</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.6400000000000006, 0.6500202799999997, 0.660...</td>\n",
- " <td>[8.477561950683594, 8.438533782958984, 8.38090...</td>\n",
- " <td>[86.12041473, 84.16366577, 82.23960876, 80.216...</td>\n",
- " <td>[]</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>-0.995987</td>\n",
- " <td>0.999664</td>\n",
- " <td>-0.999858</td>\n",
- " <td>0.948886</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>BeBe</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.4800000000000004, 0.49003191999999984, 0.50...</td>\n",
- " <td>[7.427803039550781, 7.338706970214844, 7.19796...</td>\n",
- " <td>[116.66287994, 120.23416138, 123.43974304, 126...</td>\n",
- " <td>[]</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>-0.998346</td>\n",
- " <td>0.972891</td>\n",
- " <td>-0.958827</td>\n",
- " <td>0.691532</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>BB</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.41999999999999993, 0.4300200399999996, 0.44...</td>\n",
- " <td>[1.7283554077148438, 1.9046106338500977, 2.040...</td>\n",
- " <td>[118.15281677, 121.52095032, 124.86712646, 127...</td>\n",
- " <td>[]</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>-0.970658</td>\n",
- " <td>0.922968</td>\n",
- " <td>-0.808702</td>\n",
- " <td>0.816218</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",
- " </tr>\n",
- " <tr>\n",
- " <th>112</th>\n",
- " <td>NhNh</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.09999999999999964, 0.11001697999999926, 0.1...</td>\n",
- " <td>[-2.2965879440307617, -2.30364990234375, -2.30...</td>\n",
- " <td>[-0.01143722, -0.01140489, -0.01082459, 0.1164...</td>\n",
- " <td>[]</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>-0.588028</td>\n",
- " <td>0.989775</td>\n",
- " <td>-0.215746</td>\n",
- " <td>0.963378</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>113</th>\n",
- " <td>FlFl</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.09999999999999964, 0.11001697999999926, 0.1...</td>\n",
- " <td>[-3.702641487121582, -3.683434009552002, -3.65...</td>\n",
- " <td>[-2.14600778, -2.05312586, -1.91521156, 0.2045...</td>\n",
- " <td>[]</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>-0.539312</td>\n",
- " <td>-0.457506</td>\n",
- " <td>0.870783</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>114</th>\n",
- " <td>McMc</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.09999999999999964, 0.11001697999999926, 0.1...</td>\n",
- " <td>[-1.6017036437988281, -1.5843744277954102, -1....</td>\n",
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- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
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- " <td>0.999944</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>115</th>\n",
- " <td>LvLv</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.09999999999999964, 0.11001697999999926, 0.1...</td>\n",
- " <td>[-8.688143730163574, -8.684117317199707, -8.66...</td>\n",
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- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
- " <td>NaN</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <th>116</th>\n",
- " <td>TsTs</td>\n",
- " <td>ORB</td>\n",
- " <td>[0.09999999999999964, 0.11001697999999926, 0.1...</td>\n",
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- " <td>NaN</td>\n",
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- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>117 rows × 12 columns</p>\n",
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- " name method R \\\n",
- "0 HH ORB [0.1549999999999998, 0.16504226000000033, 0.17... \n",
- "1 HeHe ORB [0.13999999999999968, 0.15003038000000046, 0.1... \n",
- "2 LiLi ORB [0.6400000000000006, 0.6500202799999997, 0.660... \n",
- "3 BeBe ORB [0.4800000000000004, 0.49003191999999984, 0.50... \n",
- "4 BB ORB [0.41999999999999993, 0.4300200399999996, 0.44... \n",
- ".. ... ... ... \n",
- "112 NhNh ORB [0.09999999999999964, 0.11001697999999926, 0.1... \n",
- "113 FlFl ORB [0.09999999999999964, 0.11001697999999926, 0.1... \n",
- "114 McMc ORB [0.09999999999999964, 0.11001697999999926, 0.1... \n",
- "115 LvLv ORB [0.09999999999999964, 0.11001697999999926, 0.1... \n",
- "116 TsTs ORB [0.09999999999999964, 0.11001697999999926, 0.1... \n",
- "\n",
- " E \\\n",
- "0 [-2.6258459091186523, -2.755453109741211, -1.8... \n",
- "1 [-0.37155914306640625, -0.4051551818847656, 0.... \n",
- "2 [8.477561950683594, 8.438533782958984, 8.38090... \n",
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- "4 [1.7283554077148438, 1.9046106338500977, 2.040... \n",
- ".. ... \n",
- "112 [-2.2965879440307617, -2.30364990234375, -2.30... \n",
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- "\n",
- " F S^2 spearman-repulsion \\\n",
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- "1 [0.02356668, -2.673e-05, 0.45423254, 1.2784304... [] NaN \n",
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- "4 [118.15281677, 121.52095032, 124.86712646, 127... [] NaN \n",
- ".. ... .. ... \n",
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- "\n",
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- ".. ... ... ... \n",
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- "\n",
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+ " <th>89</th>\n",
+ " <td>HH</td>\n",
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+ " <th>92</th>\n",
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+ " <th>175</th>\n",
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+ "176 NpNp MACE-MP(M) [8.741999999999999, 8.7319829, 8.72196582, 8.7... \n",
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