{ "cells": [ { "cell_type": "code", "execution_count": 3, "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 import MLIPEnum, REGISTRY\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 7, "id": "90887faa-1601-4c4c-9c44-d16731471d7f", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "========== eqV2(OMat) ==========\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Couldn't modify the submission pickle with error: [Errno 2] No such file or directory: '/fsx-ocp-med/lbluque/logs/omat-alex-mp/S2EFS/train/4460394/32006266_submitted.pkl'\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e77f2270a8f34d81b65cab9852bab94c", "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" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "33161320d6e5493ab57a0848eb1395a5", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/344 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='He2', pbc=True, cell=[8.866, 8.866999999999999, 8.868], calculator=SinglePointCalculator(...))\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4109a21a613a448a94f4667dd7383747", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/418 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Li2', pbc=True, cell=[13.144000000000002, 13.145000000000001, 13.146000000000003])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5c0588112db2428bac40c25c24530e67", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/542 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Be2', pbc=True, cell=[12.276, 12.277, 12.278])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cc5d8bcc3a49475fb01984ca4c768bee", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/527 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='B2', pbc=True, cell=[11.842, 11.843, 11.844000000000001])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "66d7e64e73394dc39a66ab3596fec3ca", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/516 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='C2', pbc=True, cell=[10.974, 10.975, 10.976])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8a6fa1e6419b4987ac7430e62b76234d", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/480 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='N2', pbc=True, cell=[10.292, 10.293, 10.294])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": 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"Atoms(symbols='Ne2', pbc=True, cell=[9.796000000000001, 9.797, 9.798000000000002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f1a5036eebaa4e7c9601bc2ded6e7347", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/437 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Na2', pbc=True, cell=[15.5, 15.501, 15.502])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "759939e385084be58faa4e41c6cc2648", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/625 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Mg2', pbc=True, cell=[15.562, 15.562999999999999, 15.564])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "96a4ffb0515f45539e6de783c39cd398", "version_major": 2, "version_minor": 0 }, 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11.780999999999999, 11.782])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cb5f6ecb3ca74d50b75430a0c388ad6f", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/492 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='S2', pbc=True, cell=[11.718, 11.719, 11.72])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "25b6bd606d7347ccbbc065dbac275f71", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/491 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Cl2', pbc=True, cell=[11.284, 11.285, 11.286000000000001])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "416c99cd3f864f419cd0184960a4155b", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/472 [00:00, ?it/s]" ] }, 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"application/vnd.jupyter.widget-view+json": { "model_id": "3acd1fa6108947b2bb14edf0999c89ab", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/653 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Sc2', pbc=True, cell=[15.996, 15.997, 15.998000000000001])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bec7223da3404c3a9da1cffeba23286f", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/646 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Ti2', pbc=True, cell=[15.252, 15.253, 15.254000000000001])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b25ebdf58a274614a1cecf32328f5164", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/618 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": 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[00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='U2', pbc=True, cell=[16.802, 16.803, 16.804])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "38a83f18a3f24213a8eae042bcf12019", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/663 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Np2', pbc=True, cell=[17.483999999999998, 17.485, 17.485999999999997])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2c08bcd50ca340c080e7527d571abdbe", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/703 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Pu2', pbc=True, cell=[17.422, 17.423000000000002, 17.424])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "79f1134294974bdeb837a5a011cc2d15", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/702 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Am2', pbc=True, cell=[17.546, 17.547, 17.548])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "19e13233f2d34d8d9e89913acce2821d", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/715 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='Cm2', pbc=True, cell=[18.91, 18.911, 18.912])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9896e5691f2247caabae48ae52d11ec9", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/793 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atomic number exceeds that given in model config\n", "Atoms(symbols='Bk2', pbc=True, cell=[21.08, 21.081, 21.081999999999997])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c22f12fd957d45a591d3163481a523c2", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1036 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atomic number exceeds that given in model config\n", "Atoms(symbols='Cf2', pbc=True, cell=[18.91, 18.911, 18.912])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "63b0a0d2d6494aba9446595dd9469856", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/927 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atomic number exceeds that given in model config\n", "Atoms(symbols='Es2', pbc=True, cell=[16.740000000000002, 16.741000000000003, 16.742])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "17a7a93393464381917abc7e0ced997a", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/819 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atomic number exceeds that given in model config\n", "Atoms(symbols='Fm2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9d198ddf0f95482daa5bc29cc729e43f", "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": [ "Atomic number exceeds that given in model config\n", "Atoms(symbols='Md2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": 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"output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Atomic number exceeds that given in model config\n", "Atoms(symbols='Og2', pbc=True, cell=[12.0, 12.001, 12.002])\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cefa8ab6ce3f449f8b882198315c971a", "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": [ "Atomic number exceeds that given in model config\n" ] } ], "source": [ "for model in MLIPEnum:\n", " \n", " model_name = model.name\n", " \n", " if model_name != 'eqV2(OMat)':\n", " continue\n", " \n", " print(f\"========== {model_name} ==========\")\n", "\n", " calc = MLIPEnum[model_name].value()\n", "\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": 8, "id": "a0ac2c09-370b-4fdd-bf74-ea5c4ade0215", "metadata": { "scrolled": true, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "========== eqV2(OMat) ==========\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c23d83b4331c43e3bceb6d786f65d9ec", "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_1500708/3508918110.py:164: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", " df = pd.concat([df, pd.DataFrame([data])], ignore_index=True)\n" ] } ], "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": 9, "id": "e0dd4367-3dca-440f-a7a9-7fdd84183f2c", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", " | name | \n", "method | \n", "R | \n", "E | \n", "F | \n", "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", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "HH | \n", "eqV2(OMat) | \n", "[3.7199999999999998, 3.70996794, 3.69993586, 3... | \n", "[-2.0984511375427246, -2.095881462097168, -2.0... | \n", "[9.72e-06, 7.52e-06, 3.211e-05, 3.564e-05, 3.9... | \n", "[] | \n", "2 | \n", "106.606564 | \n", "1.924082 | \n", "17 | \n", "93.241588 | \n", "0.564488 | \n", "18.840863 | \n", "1.555587 | \n", "2.842868 | \n", "-0.994359 | \n", "-0.072700 | \n", "-0.992857 | \n", "0.431210 | \n", "
1 | \n", "HeHe | \n", "eqV2(OMat) | \n", "[4.433, 4.4229736200000005, 4.41294724, 4.4029... | \n", "[0.5383987426757812, 0.5361838340759277, 0.536... | \n", "[-1.365e-05, -4.945e-05, -8.538e-05, -0.000155... | \n", "[] | \n", "2 | \n", "265.816823 | \n", "5.186691 | \n", "14 | \n", "77.250589 | \n", "0.699341 | \n", "19.451416 | \n", "4.166659 | \n", "10.740978 | \n", "-0.973671 | \n", "-0.546770 | \n", "-0.912122 | \n", "0.600920 | \n", "
2 | \n", "LiLi | \n", "eqV2(OMat) | \n", "[6.572000000000001, 6.561981520000001, 6.55196... | \n", "[-0.5126352310180664, -0.5116205215454102, -0.... | \n", "[-3.34e-05, 0.00016922, 0.00028867, 0.0005224,... | \n", "[] | \n", "1 | \n", "28.116090 | \n", "0.053238 | \n", "31 | \n", "17.085735 | \n", "0.181582 | \n", "7.866485 | \n", "1.334280 | \n", "1.063989 | \n", "-0.998354 | \n", "0.976426 | \n", "-0.981398 | \n", "0.705693 | \n", "
3 | \n", "BeBe | \n", "eqV2(OMat) | \n", "[6.138000000000001, 6.12797338, 6.117946759999... | \n", "[0.23909759521484375, 0.2400522232055664, 0.23... | \n", "[-4.817e-05, 0.00051783, 0.00090912, 0.0012907... | \n", "[] | \n", "1 | \n", "188.239996 | \n", "0.177490 | \n", "35 | \n", "22.672879 | \n", "0.328797 | \n", "13.735467 | \n", "1.275790 | \n", "6.498375 | \n", "-0.997688 | \n", "0.983071 | \n", "-0.972891 | \n", "0.014737 | \n", "
4 | \n", "BB | \n", "eqV2(OMat) | \n", "[5.921000000000001, 5.91097088, 5.900941739999... | \n", "[-1.388545036315918, -1.3869714736938477, -1.3... | \n", "[-7.486e-05, -0.00062429, -0.00124062, -0.0016... | \n", "[] | \n", "1 | \n", "142.788128 | \n", "0.010691 | \n", "19 | \n", "30.771105 | \n", "0.572540 | \n", "13.649937 | \n", "1.145869 | \n", "4.867476 | \n", "-1.000000 | \n", "0.990445 | \n", "-0.984379 | \n", "0.993144 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
90 | \n", "PaPa | \n", "eqV2(OMat) | \n", "[8.927999999999999, 8.91797468, 8.90794936, 8.... | \n", "[-8.536531448364258, -8.537920951843262, -8.55... | \n", "[0.00149543, 0.00933722, 0.01594199, 0.0210447... | \n", "[] | \n", "14 | \n", "293.319609 | \n", "0.622614 | \n", "47 | \n", "94.871632 | \n", "1.276346 | \n", "37.647346 | \n", "3.460726 | \n", "9.596265 | \n", "-0.976682 | \n", "0.916942 | \n", "-1.000000 | \n", "0.064439 | \n", "
91 | \n", "UU | \n", "eqV2(OMat) | \n", "[8.401, 8.390974320000002, 8.38094864, 8.37092... | \n", "[-12.306029319763184, -12.309735298156738, -12... | \n", "[0.00250636, -0.00677688, -0.01450382, -0.0195... | \n", "[] | \n", "9 | \n", "308.464112 | \n", "1.041690 | \n", "53 | \n", "74.002914 | \n", "2.503158 | \n", "36.593965 | \n", "3.514434 | \n", "9.661410 | \n", "-0.853855 | \n", "0.856809 | \n", "-1.000000 | \n", "0.009604 | \n", "
92 | \n", "NpNp | \n", "eqV2(OMat) | \n", "[8.741999999999999, 8.7319829, 8.72196582, 8.7... | \n", "[-16.419757843017578, -16.415634155273438, -16... | \n", "[0.00010047, 0.00343876, 0.00583172, 0.0066036... | \n", "[] | \n", "13 | \n", "366.700701 | \n", "2.488891 | \n", "58 | \n", "145.748637 | \n", "3.159492 | \n", "68.312851 | \n", "6.846896 | \n", "14.868774 | \n", "-0.970815 | \n", "0.783776 | \n", "-1.000000 | \n", "-0.092599 | \n", "
93 | \n", "PuPu | \n", "eqV2(OMat) | \n", "[8.710999999999999, 8.70097432, 8.690948639999... | \n", "[-21.575969696044922, -21.568323135375977, -21... | \n", "[0.00021465, -0.018026, -0.02833581, -0.022387... | \n", "[] | \n", "25 | \n", "404.453349 | \n", "20.662893 | \n", "85 | \n", "195.927256 | \n", "14.399994 | \n", "78.172195 | \n", "5.575810 | \n", "15.614091 | \n", "-0.661178 | \n", "0.348212 | \n", "-0.002303 | \n", "0.654905 | \n", "
94 | \n", "AmAm | \n", "eqV2(OMat) | \n", "[8.773, 8.7629818, 8.75296358, 8.7429453800000... | \n", "[8.622910499572754, 8.642560958862305, 8.69469... | \n", "[-0.00028193, -0.00328918, -0.00321345, 0.0024... | \n", "[] | \n", "8 | \n", "63.571266 | \n", "0.964731 | \n", "58 | \n", "50.950555 | \n", "2.227477 | \n", "30.767544 | \n", "2.925538 | \n", "3.732556 | \n", "-0.999440 | \n", "0.872640 | \n", "-0.995198 | \n", "0.738493 | \n", "
95 rows × 19 columns
\n", "