{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3200850a-b8fb-4f50-9815-16ae8da0f942",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ase import Atom, Atoms\n",
    "from ase.data import chemical_symbols, covalent_radii, vdw_alvarez\n",
    "from ase.io import read, write\n",
    "from pymatgen.core import Element\n",
    "from scipy import stats\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from mlip_arena.models.utils import REGISTRY, MLIPEnum\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# model_name = \"MACE-MP(M)\"\n",
    "\n",
    "# calc = MLIPEnum[model_name].value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90887faa-1601-4c4c-9c44-d16731471d7f",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== eSCN(OC20) ==========\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.\n",
      "WARNING:root:Couldn't modify the submission pickle with error: [Errno 2] No such file or directory: '/checkpoint/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"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atoms(symbols='H2', pbc=True, cell=[7.4399999999999995, 7.441, 7.441999999999999])\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Atoms(symbols='He2', pbc=True, cell=[8.866, 8.866999999999999, 8.868])\n"
     ]
    },
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       "  0%|          | 0/418 [00:00<?, ?it/s]"
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     "output_type": "stream",
     "text": [
      "Atoms(symbols='Li2', pbc=True, cell=[13.144000000000002, 13.145000000000001, 13.146000000000003])\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Atoms(symbols='Be2', pbc=True, cell=[12.276, 12.277, 12.278])\n"
     ]
    },
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     "text": [
      "Atoms(symbols='B2', pbc=True, cell=[11.842, 11.843, 11.844000000000001])\n"
     ]
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     "text": [
      "Atoms(symbols='C2', pbc=True, cell=[10.974, 10.975, 10.976])\n"
     ]
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     "text": [
      "Atoms(symbols='N2', pbc=True, cell=[10.292, 10.293, 10.294])\n"
     ]
    },
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     "text": [
      "Atoms(symbols='O2', pbc=True, cell=[9.3, 9.301, 9.302000000000001])\n"
     ]
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      "Atoms(symbols='F2', pbc=True, cell=[9.052, 9.052999999999999, 9.054])\n"
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     ]
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      "Atoms(symbols='Na2', pbc=True, cell=[15.5, 15.501, 15.502])\n"
     ]
    },
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     "text": [
      "Atoms(symbols='Mg2', pbc=True, cell=[15.562, 15.562999999999999, 15.564])\n"
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       "  0%|          | 0/651 [00:00<?, ?it/s]"
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      "Atoms(symbols='Ti2', pbc=True, cell=[15.252, 15.253, 15.254000000000001])\n"
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      "Atoms(symbols='V2', pbc=True, cell=[15.004, 15.004999999999999, 15.006])\n"
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      "Atoms(symbols='As2', pbc=True, cell=[11.655999999999999, 11.656999999999998, 11.658])\n"
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     "text": [
      "Atoms(symbols='Pb2', pbc=True, cell=[16.12, 16.121000000000002, 16.122])\n"
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     "text": [
      "Atoms(symbols='Bi2', pbc=True, cell=[15.748000000000001, 15.749, 15.750000000000002])\n"
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     "text": [
      "Atoms(symbols='Po2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "text": [
      "Atoms(symbols='At2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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    {
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     "text": [
      "Atoms(symbols='Rn2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "text": [
      "Atoms(symbols='Fr2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "text": [
      "Atoms(symbols='Ra2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "data": {
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     "text": [
      "Atoms(symbols='Ac2', pbc=True, cell=[17.36, 17.361, 17.362])\n"
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     "text": [
      "Atoms(symbols='Th2', pbc=True, cell=[18.166, 18.167, 18.168])\n"
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     ]
    },
    {
     "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"
     ]
    },
    {
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     "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"
     ]
    },
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     "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"
     ]
    },
    {
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    {
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     "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"
     ]
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    {
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     "metadata": {},
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     "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"
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    {
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     "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"
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    {
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     "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='Bk2', pbc=True, cell=[21.08, 21.081, 21.081999999999997])\n"
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     "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='Cf2', pbc=True, cell=[18.91, 18.911, 18.912])\n"
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     "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='Es2', pbc=True, cell=[16.740000000000002, 16.741000000000003, 16.742])\n"
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     "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='Fm2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "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='Md2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "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='No2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "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='Lr2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "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='Rf2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "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='Db2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "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='Sg2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "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='Bh2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "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='Hs2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
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     "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='Mt2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7558626dbc342ccaf5b37541c8e1575",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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='Ds2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac000438284a47f2b45e212225141349",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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='Rg2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7d6ab0bbd4149b1a27563d45cf7715d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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='Cn2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "457140990a5f47ad9e7047cb6006056f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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='Nh2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9db17a0c4cb84a6d9d6bd8752d5d18c6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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='Fl2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bacec72e6c8c4dd2bb2d582180ca2f60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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='Mc2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8b2f170eb334afab30b7409d3f8c58b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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='Lv2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e3cbc309764417ab4b4585b6a5280d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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='Ts2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "280942de0d284c13bfb0e87f8865a5f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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='Og2', pbc=True, cell=[12.0, 12.001, 12.002])\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d255dfff643f481486ec79c4f149f763",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/582 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    }
   ],
   "source": [
    "for model in MLIPEnum:\n",
    "    \n",
    "    model_name = model.name\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": 2,
   "id": "a0ac2c09-370b-4fdd-bf74-ea5c4ade0215",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== MACE-MP(M) ==========\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55562f662e56481ba346246ccc109626",
       "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: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"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "for model in MLIPEnum:\n",
    "    \n",
    "    model_name = model.name\n",
    "    \n",
    "#     json_fpath = Path(REGISTRY[model_name][\"family\"]) / \"homonuclear-diatomics.json\"\n",
    "    \n",
    "#     if json_fpath.exists():\n",
    "#         continue\n",
    "\n",
    "    print(f\"========== {model_name} ==========\")\n",
    "    \n",
    "    df = pd.DataFrame(columns=[\n",
    "        \"name\", \n",
    "        \"method\", \n",
    "        \"R\", \"E\", \"F\", \"S^2\", \n",
    "        \"force-flip-times\",\n",
    "        \"force-total-variation\",\n",
    "        \"force-jump\",\n",
    "        \"energy-diff-flip-times\",\n",
    "        \"energy-grad-norm-max\",\n",
    "        \"energy-jump\",\n",
    "        \"energy-total-variation\",\n",
    "        \"tortuosity\",\n",
    "        \"conservation-deviation\",\n",
    "        \"spearman-descending-force\",\n",
    "        \"spearman-ascending-force\",\n",
    "        \"spearman-repulsion-energy\",\n",
    "        \"spearman-attraction-energy\"\n",
    "    ])\n",
    "    \n",
    "\n",
    "    for symbol in tqdm(chemical_symbols[1:]):\n",
    "\n",
    "        da = symbol + symbol\n",
    "\n",
    "        out_dir = Path(REGISTRY[model_name][\"family\"]) / da\n",
    "\n",
    "        traj_fpath = out_dir / f\"{model_name}.extxyz\"\n",
    "\n",
    "\n",
    "        if traj_fpath.exists():\n",
    "            traj = read(traj_fpath, index=\":\")\n",
    "        else:\n",
    "            continue\n",
    "\n",
    "        Rs, Es, Fs, S2s = [], [], [], []\n",
    "        for atoms in traj:\n",
    "\n",
    "            vec = atoms.positions[1] - atoms.positions[0]\n",
    "            r = np.linalg.norm(vec)\n",
    "            e = atoms.get_potential_energy()\n",
    "            f = np.inner(vec/r, atoms.get_forces()[1])\n",
    "            # s2 = np.mean(np.power(atoms.get_magnetic_moments(), 2))\n",
    "\n",
    "            Rs.append(r)\n",
    "            Es.append(e)\n",
    "            Fs.append(f)\n",
    "            # S2s.append(s2)\n",
    "\n",
    "        rs = np.array(Rs)\n",
    "        es = np.array(Es)\n",
    "        fs = np.array(Fs)\n",
    "\n",
    "        indices = np.argsort(rs)[::-1]\n",
    "        rs = rs[indices]\n",
    "        es = es[indices]\n",
    "        eshift = es[0]\n",
    "        es -= eshift\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",
    "        mask = fs_sign != 0\n",
    "        rounded_fs = rounded_fs[mask]\n",
    "        fs_sign = fs_sign[mask]\n",
    "        f_flip = np.diff(fs_sign) != 0\n",
    "        \n",
    "        fdiff = np.diff(fs)\n",
    "        fdiff_sign = np.sign(fdiff)\n",
    "        mask = fdiff_sign != 0\n",
    "        fdiff = fdiff[mask]\n",
    "        fdiff_sign = fdiff_sign[mask]\n",
    "        fdiff_flip = np.diff(fdiff_sign) != 0\n",
    "        fjump = np.abs(fdiff[:-1][fdiff_flip]).sum() + np.abs(fdiff[1:][fdiff_flip]).sum()\n",
    "        \n",
    "\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",
    "        \n",
    "#         edged_es = np.convolve(es, [1, -2, 1], mode='valid')\n",
    "#         # edged_es[np.abs(edged_es) < 0.1] = 0\n",
    "#         prob = np.exp(-es[1:-1]) / np.sum(np.exp(-es[1:-1]))\n",
    "#         edged_es *= prob\n",
    "#         # edged_es /= np.abs(es[1:-1])\n",
    "#         ejump = np.linalg.norm(edged_es)\n",
    "#         ejump = np.abs(edged_es).sum() / 2.0\n",
    "        \n",
    "#         edged_fs = np.convolve(fs, [1, -2, 1], mode='valid')\n",
    "#         # edged_fs[np.abs(edged_fs) < 0.1] = 0\n",
    "#         edged_fs *= prob\n",
    "#         fjump = np.linalg.norm(edged_fs)\n",
    "        # fjump = np.abs(edged_fs).sum() / 2.0\n",
    "        \n",
    "#         fig, axes = plt.subplot_mosaic(\n",
    "#             \"\"\"\n",
    "#             ac\n",
    "#             bd\n",
    "#             \"\"\",\n",
    "#             constrained_layout=True\n",
    "#         )\n",
    "        \n",
    "\n",
    "#         axes['a'].plot(rs, es)\n",
    "#         axes['b'].plot(rs[1:-1], edged_es)\n",
    "#         # axes['b'].plot(0.5*(rs[1:] + rs[:-1]), np.diff(es))\n",
    "#         axes['b'].text(0.7, 0.7, f\"{ejump:.3e}\", transform=axes['b'].transAxes)\n",
    "        \n",
    "#         axes['c'].plot(rs, fs)\n",
    "#         axes['d'].plot(rs[1:-1], edged_fs)\n",
    "#         axes['d'].text(0.7, 0.7, f\"{fjump:.3e}\", transform=axes['d'].transAxes)\n",
    "        \n",
    "\n",
    "        conservation_deviation = np.mean(np.abs(fs + de_dr))\n",
    "        \n",
    "        etv = np.sum(np.abs(np.diff(es)))\n",
    "\n",
    "        data = {\n",
    "            \"name\": da,\n",
    "            \"method\": model_name,\n",
    "            \"R\": rs,\n",
    "            \"E\": es + eshift,\n",
    "            \"F\": fs,\n",
    "            \"S^2\": S2s,\n",
    "            \"force-flip-times\": np.sum(f_flip),\n",
    "            \"force-total-variation\": np.sum(np.abs(np.diff(fs))),\n",
    "            \"force-jump\": fjump,\n",
    "            \"energy-diff-flip-times\": np.sum(ediff_flip),\n",
    "            \"energy-grad-norm-max\": np.max(np.abs(de_dr)),\n",
    "            \"energy-jump\": ejump,\n",
    "            # \"energy-grad-norm-mean\": np.mean(de_dr_abs),\n",
    "            \"energy-total-variation\": etv,\n",
    "            \"tortuosity\": etv / (abs(es[0] - es.min()) + (es[-1] - es.min())),\n",
    "            \"conservation-deviation\": conservation_deviation,\n",
    "            \"spearman-descending-force\": stats.spearmanr(rs[iminf:], fs[iminf:]).statistic,\n",
    "            \"spearman-ascending-force\": stats.spearmanr(rs[:iminf], fs[:iminf]).statistic,\n",
    "            \"spearman-repulsion-energy\": stats.spearmanr(rs[imine:], es[imine:]).statistic,\n",
    "            \"spearman-attraction-energy\": stats.spearmanr(rs[:imine], es[:imine]).statistic,\n",
    "        }\n",
    "\n",
    "        df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n",
    "\n",
    "    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": 3,
   "id": "e0dd4367-3dca-440f-a7a9-7fdd84183f2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>method</th>\n",
       "      <th>R</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>S^2</th>\n",
       "      <th>force-flip-times</th>\n",
       "      <th>force-total-variation</th>\n",
       "      <th>energy-diff-flip-times</th>\n",
       "      <th>energy-grad-norm-max</th>\n",
       "      <th>energy-jump</th>\n",
       "      <th>energy-total-variation</th>\n",
       "      <th>conservation-deviation</th>\n",
       "      <th>spearman-descending-force</th>\n",
       "      <th>spearman-ascending-force</th>\n",
       "      <th>spearman-repulsion-energy</th>\n",
       "      <th>spearman-attraction-energy</th>\n",
       "      <th>tortuosity</th>\n",
       "      <th>force-jump</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>HH</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[3.7199999999999998, 3.70996794, 3.69993586, 3...</td>\n",
       "      <td>[-1.2249419689178467, -1.2238645553588867, -1....</td>\n",
       "      <td>[1.91e-06, 0.00826454, 0.00533009, -0.00355052...</td>\n",
       "      <td>[]</td>\n",
       "      <td>29</td>\n",
       "      <td>443.614255</td>\n",
       "      <td>30</td>\n",
       "      <td>40.656501</td>\n",
       "      <td>4.074248</td>\n",
       "      <td>13.676080</td>\n",
       "      <td>2.022960</td>\n",
       "      <td>-0.200684</td>\n",
       "      <td>-0.119952</td>\n",
       "      <td>-0.986572</td>\n",
       "      <td>0.844786</td>\n",
       "      <td>1.797762</td>\n",
       "      <td>458.210954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>HeHe</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[4.433, 4.4229736200000005, 4.41294724, 4.4029...</td>\n",
       "      <td>[2.4748411178588867, 2.4748411178588867, 2.474...</td>\n",
       "      <td>[0.0, -1e-08, 0.0, 0.0, 1e-08, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>44</td>\n",
       "      <td>1448.979436</td>\n",
       "      <td>43</td>\n",
       "      <td>160.175544</td>\n",
       "      <td>13.831072</td>\n",
       "      <td>32.544741</td>\n",
       "      <td>4.849522</td>\n",
       "      <td>-0.021494</td>\n",
       "      <td>-0.195001</td>\n",
       "      <td>-0.720218</td>\n",
       "      <td>0.609519</td>\n",
       "      <td>3.921846</td>\n",
       "      <td>1582.685197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>LiLi</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[6.572000000000001, 6.561981520000001, 6.55196...</td>\n",
       "      <td>[-0.21738338470458984, -0.21738338470458984, -...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>22</td>\n",
       "      <td>1127.749591</td>\n",
       "      <td>20</td>\n",
       "      <td>281.659136</td>\n",
       "      <td>2.067203</td>\n",
       "      <td>20.053466</td>\n",
       "      <td>2.469147</td>\n",
       "      <td>-0.714286</td>\n",
       "      <td>-0.444127</td>\n",
       "      <td>-0.991010</td>\n",
       "      <td>-0.654715</td>\n",
       "      <td>1.386696</td>\n",
       "      <td>1721.766498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>BeBe</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[6.138000000000001, 6.12797338, 6.117946759999...</td>\n",
       "      <td>[2.665587902069092, 2.665587902069092, 2.66558...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 1e-08, 0.0, 0.0, 0.0, 0.0...</td>\n",
       "      <td>[]</td>\n",
       "      <td>25</td>\n",
       "      <td>1645.960357</td>\n",
       "      <td>21</td>\n",
       "      <td>145.040462</td>\n",
       "      <td>9.422094</td>\n",
       "      <td>51.647955</td>\n",
       "      <td>5.076523</td>\n",
       "      <td>0.544090</td>\n",
       "      <td>-0.158055</td>\n",
       "      <td>-0.989962</td>\n",
       "      <td>0.342476</td>\n",
       "      <td>1.715953</td>\n",
       "      <td>1775.001526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>BB</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[5.921000000000001, 5.91097088, 5.900941739999...</td>\n",
       "      <td>[0.6220548152923584, 0.6220548152923584, 0.622...</td>\n",
       "      <td>[0.0, 0.0, 1e-08, 0.0, 0.0, 1e-08, -1e-08, -1e...</td>\n",
       "      <td>[]</td>\n",
       "      <td>35</td>\n",
       "      <td>1811.413732</td>\n",
       "      <td>36</td>\n",
       "      <td>131.340791</td>\n",
       "      <td>13.088368</td>\n",
       "      <td>52.691856</td>\n",
       "      <td>5.551787</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>-0.171460</td>\n",
       "      <td>-0.985024</td>\n",
       "      <td>0.757880</td>\n",
       "      <td>1.617372</td>\n",
       "      <td>1799.183469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>FlFl</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
       "      <td>[]</td>\n",
       "      <td>39</td>\n",
       "      <td>3329.770831</td>\n",
       "      <td>44</td>\n",
       "      <td>181.338142</td>\n",
       "      <td>12.292844</td>\n",
       "      <td>84.239434</td>\n",
       "      <td>8.151059</td>\n",
       "      <td>-0.249255</td>\n",
       "      <td>-0.316556</td>\n",
       "      <td>-0.946770</td>\n",
       "      <td>0.159576</td>\n",
       "      <td>2.694147</td>\n",
       "      <td>3926.252725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>McMc</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
       "      <td>[]</td>\n",
       "      <td>39</td>\n",
       "      <td>3329.770831</td>\n",
       "      <td>44</td>\n",
       "      <td>181.338142</td>\n",
       "      <td>12.292844</td>\n",
       "      <td>84.239434</td>\n",
       "      <td>8.151059</td>\n",
       "      <td>-0.249255</td>\n",
       "      <td>-0.316556</td>\n",
       "      <td>-0.946770</td>\n",
       "      <td>0.159576</td>\n",
       "      <td>2.694147</td>\n",
       "      <td>3926.252725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>LvLv</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
       "      <td>[]</td>\n",
       "      <td>39</td>\n",
       "      <td>3329.770831</td>\n",
       "      <td>44</td>\n",
       "      <td>181.338142</td>\n",
       "      <td>12.292844</td>\n",
       "      <td>84.239434</td>\n",
       "      <td>8.151059</td>\n",
       "      <td>-0.249255</td>\n",
       "      <td>-0.316556</td>\n",
       "      <td>-0.946770</td>\n",
       "      <td>0.159576</td>\n",
       "      <td>2.694147</td>\n",
       "      <td>3926.252725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>TsTs</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
       "      <td>[]</td>\n",
       "      <td>39</td>\n",
       "      <td>3329.770831</td>\n",
       "      <td>44</td>\n",
       "      <td>181.338142</td>\n",
       "      <td>12.292844</td>\n",
       "      <td>84.239434</td>\n",
       "      <td>8.151059</td>\n",
       "      <td>-0.249255</td>\n",
       "      <td>-0.316556</td>\n",
       "      <td>-0.946770</td>\n",
       "      <td>0.159576</td>\n",
       "      <td>2.694147</td>\n",
       "      <td>3926.252725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>OgOg</td>\n",
       "      <td>ALIGNN</td>\n",
       "      <td>[6.0, 5.989982779999999, 5.979965579999999, 5....</td>\n",
       "      <td>[-10.127323150634766, -10.127323150634766, -10...</td>\n",
       "      <td>[0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...</td>\n",
       "      <td>[]</td>\n",
       "      <td>39</td>\n",
       "      <td>3329.770831</td>\n",
       "      <td>44</td>\n",
       "      <td>181.338142</td>\n",
       "      <td>12.292844</td>\n",
       "      <td>84.239434</td>\n",
       "      <td>8.151059</td>\n",
       "      <td>-0.249255</td>\n",
       "      <td>-0.316556</td>\n",
       "      <td>-0.946770</td>\n",
       "      <td>0.159576</td>\n",
       "      <td>2.694147</td>\n",
       "      <td>3926.252725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  method                                                  R  \\\n",
       "118    HH  ALIGNN  [3.7199999999999998, 3.70996794, 3.69993586, 3...   \n",
       "119  HeHe  ALIGNN  [4.433, 4.4229736200000005, 4.41294724, 4.4029...   \n",
       "120  LiLi  ALIGNN  [6.572000000000001, 6.561981520000001, 6.55196...   \n",
       "121  BeBe  ALIGNN  [6.138000000000001, 6.12797338, 6.117946759999...   \n",
       "122    BB  ALIGNN  [5.921000000000001, 5.91097088, 5.900941739999...   \n",
       "..    ...     ...                                                ...   \n",
       "231  FlFl  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
       "232  McMc  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
       "233  LvLv  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
       "234  TsTs  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
       "235  OgOg  ALIGNN  [6.0, 5.989982779999999, 5.979965579999999, 5....   \n",
       "\n",
       "                                                     E  \\\n",
       "118  [-1.2249419689178467, -1.2238645553588867, -1....   \n",
       "119  [2.4748411178588867, 2.4748411178588867, 2.474...   \n",
       "120  [-0.21738338470458984, -0.21738338470458984, -...   \n",
       "121  [2.665587902069092, 2.665587902069092, 2.66558...   \n",
       "122  [0.6220548152923584, 0.6220548152923584, 0.622...   \n",
       "..                                                 ...   \n",
       "231  [-10.127323150634766, -10.127323150634766, -10...   \n",
       "232  [-10.127323150634766, -10.127323150634766, -10...   \n",
       "233  [-10.127323150634766, -10.127323150634766, -10...   \n",
       "234  [-10.127323150634766, -10.127323150634766, -10...   \n",
       "235  [-10.127323150634766, -10.127323150634766, -10...   \n",
       "\n",
       "                                                     F S^2 force-flip-times  \\\n",
       "118  [1.91e-06, 0.00826454, 0.00533009, -0.00355052...  []               29   \n",
       "119  [0.0, -1e-08, 0.0, 0.0, 1e-08, 0.0, 0.0, 0.0, ...  []               44   \n",
       "120  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  []               22   \n",
       "121  [0.0, 0.0, 0.0, 0.0, 1e-08, 0.0, 0.0, 0.0, 0.0...  []               25   \n",
       "122  [0.0, 0.0, 1e-08, 0.0, 0.0, 1e-08, -1e-08, -1e...  []               35   \n",
       "..                                                 ...  ..              ...   \n",
       "231  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
       "232  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
       "233  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
       "234  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
       "235  [0.0, 0.0, -9.5e-07, 0.0, 0.0, 0.0, 0.0, 9.5e-...  []               39   \n",
       "\n",
       "     force-total-variation energy-diff-flip-times  energy-grad-norm-max  \\\n",
       "118             443.614255                     30             40.656501   \n",
       "119            1448.979436                     43            160.175544   \n",
       "120            1127.749591                     20            281.659136   \n",
       "121            1645.960357                     21            145.040462   \n",
       "122            1811.413732                     36            131.340791   \n",
       "..                     ...                    ...                   ...   \n",
       "231            3329.770831                     44            181.338142   \n",
       "232            3329.770831                     44            181.338142   \n",
       "233            3329.770831                     44            181.338142   \n",
       "234            3329.770831                     44            181.338142   \n",
       "235            3329.770831                     44            181.338142   \n",
       "\n",
       "     energy-jump  energy-total-variation  conservation-deviation  \\\n",
       "118     4.074248               13.676080                2.022960   \n",
       "119    13.831072               32.544741                4.849522   \n",
       "120     2.067203               20.053466                2.469147   \n",
       "121     9.422094               51.647955                5.076523   \n",
       "122    13.088368               52.691856                5.551787   \n",
       "..           ...                     ...                     ...   \n",
       "231    12.292844               84.239434                8.151059   \n",
       "232    12.292844               84.239434                8.151059   \n",
       "233    12.292844               84.239434                8.151059   \n",
       "234    12.292844               84.239434                8.151059   \n",
       "235    12.292844               84.239434                8.151059   \n",
       "\n",
       "     spearman-descending-force  spearman-ascending-force  \\\n",
       "118                  -0.200684                 -0.119952   \n",
       "119                  -0.021494                 -0.195001   \n",
       "120                  -0.714286                 -0.444127   \n",
       "121                   0.544090                 -0.158055   \n",
       "122                   0.052632                 -0.171460   \n",
       "..                         ...                       ...   \n",
       "231                  -0.249255                 -0.316556   \n",
       "232                  -0.249255                 -0.316556   \n",
       "233                  -0.249255                 -0.316556   \n",
       "234                  -0.249255                 -0.316556   \n",
       "235                  -0.249255                 -0.316556   \n",
       "\n",
       "     spearman-repulsion-energy  spearman-attraction-energy  tortuosity  \\\n",
       "118                  -0.986572                    0.844786    1.797762   \n",
       "119                  -0.720218                    0.609519    3.921846   \n",
       "120                  -0.991010                   -0.654715    1.386696   \n",
       "121                  -0.989962                    0.342476    1.715953   \n",
       "122                  -0.985024                    0.757880    1.617372   \n",
       "..                         ...                         ...         ...   \n",
       "231                  -0.946770                    0.159576    2.694147   \n",
       "232                  -0.946770                    0.159576    2.694147   \n",
       "233                  -0.946770                    0.159576    2.694147   \n",
       "234                  -0.946770                    0.159576    2.694147   \n",
       "235                  -0.946770                    0.159576    2.694147   \n",
       "\n",
       "      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",
       "\n",
       "[118 rows x 19 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e6ae884-89f3-43f2-8fd9-19bf00c91566",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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