diff --git a/notebooks/proof-of-concept/2_approximate-hessians.ipynb b/notebooks/proof-of-concept/2_approximate-hessians.ipynb deleted file mode 100644 index 2de0f82..0000000 --- a/notebooks/proof-of-concept/2_approximate-hessians.ipynb +++ /dev/null @@ -1,584 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "201346ec-3f5a-4235-b8ef-4a0051373865", - "metadata": {}, - "source": [ - "# Generate Approximate Hessians\n", - "Use the random displacement data to get the full Hessian matrix. We assume that many parameters of the Hessian matrix are zero, and therefore that we can fit the Hessian matrix with a reduced number of points using [sparse linear regression](https://scikit-learn.org/stable/modules/linear_model.html#lasso)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ebbbc7f5-3007-420f-861a-9f65f84436be", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from matplotlib import pyplot as plt\n", - "from jitterbug.model.linear import HarmonicModel\n", - "from jitterbug.model.linear import get_model_inputs\n", - "from sklearn.linear_model import LinearRegression, ElasticNetCV\n", - "from ase.vibrations import VibrationsData\n", - "from ase.db import connect\n", - "from pathlib import Path\n", - "from tqdm import tqdm\n", - "import numpy as np\n", - "import warnings" - ] - }, - { - "cell_type": "markdown", - "id": "85a147c1-2758-465b-bc54-dc373d73a0f3", - "metadata": {}, - "source": [ - "Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "99bd4c92-9a7b-4e88-ac45-dbf30fbfc9e0", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "molecule_name = 'water'\n", - "method = 'hf'\n", - "basis = 'def2-svpd'\n", - "step_size: float = 0.005 # Perturbation amount, used as maximum L2 norm\n", - "regressor = LinearRegression" - ] - }, - { - "cell_type": "markdown", - "id": "8505d400-8427-45b9-b626-3f9ca557d0c8", - "metadata": {}, - "source": [ - "Derived" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a8be3c37-bf1f-4ba4-ba8f-afff6d6bed7d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "run_name = f'{molecule_name}_{method}_{basis}'\n", - "out_dir = Path('data') / 'approx'\n", - "db_path = out_dir / f'{run_name}-random-d={step_size:.2e}.db'" - ] - }, - { - "cell_type": "markdown", - "id": "de1f6aac-b93e-45a7-98e6-ffd5205916a6", - "metadata": {}, - "source": [ - "## Read in the Data\n", - "Get all computations for the desired calculation and the exact solution" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "797b96d8-050c-4bdf-9815-586cfb5bc311", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded 46 structures\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 1.35768557e+03, 5.13074732e+02, -1.78611307e+03, -3.68960372e+02,\n", - " 4.06940665e+02, -5.02709874e+02, 9.81422728e+02, -7.49646372e+02,\n", - " 2.31747899e+03, 9.21655058e+05, 6.96594162e+05, -2.42497995e+06,\n", - " -5.00932174e+05, 5.52497470e+05, -6.82521944e+05, 1.33246348e+06,\n", - " -1.01778406e+06, 3.14640779e+06, 1.31622840e+05, -9.16409487e+05,\n", - " -1.89304244e+05, 2.08790973e+05, -2.57927734e+05, 5.03543203e+05,\n", - " -3.84624612e+05, 1.18903991e+06, 1.59509996e+06, 6.59004944e+05,\n", - " -7.26842042e+05, 8.97896679e+05, -1.75293196e+06, 1.33895319e+06,\n", - " -4.13927952e+06, 6.80658780e+04, -1.50144979e+05, 1.85480022e+05,\n", - " -3.62106095e+05, 2.76589804e+05, -8.55057910e+05, 8.28003524e+04,\n", - " -2.04573091e+05, 3.99380817e+05, -3.05061593e+05, 9.43076441e+05,\n", - " 1.26358609e+05, -4.93370896e+05, 3.76854634e+05, -1.16501957e+06,\n", - " 4.81595285e+05, -7.35719987e+05, 2.27442655e+06, 2.80984842e+05,\n", - " -1.73728972e+06, 2.68535444e+06])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with connect(db_path) as db:\n", - " data = [a.toatoms() for a in db.select('')]\n", - "print(f'Loaded {len(data)} structures')\n", - "get_model_inputs(data[1], data[0])" - ] - }, - { - "cell_type": "markdown", - "id": "cb1a8e03-b045-49a4-95fd-61636a48fbad", - "metadata": {}, - "source": [ - "Read in the exact Hessian" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7389208d-9323-492c-8fc5-d05a372206c6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "with open(f'data/exact/{run_name}-ase.json') as fp:\n", - " exact_vibs = VibrationsData.read(fp)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a9965595-532c-4067-ba24-7620bd977007", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "exact_hess = exact_vibs.get_hessian_2d()\n", - "exact_zpe = exact_vibs.get_zero_point_energy()\n", - "exact_freqs = exact_vibs.get_frequencies()" - ] - }, - { - "cell_type": "markdown", - "id": "04c60da8-4a1d-4ae3-b45d-b77e71fd598f", - "metadata": {}, - "source": [ - "## Fit a Hessian with All Data\n", - "Fit a model which explains the energy data by fitting a Hessian matrix using compressed sensing (i.e., Lasso)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d6a7d756-37d3-44e0-b5e2-348d07c9d296", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/elliott/Packages/faster-molecular-hessians/jitterbug/model/linear.py:27: RuntimeWarning: divide by zero encountered in reciprocal\n", - " disp_matrix = np.reciprocal((atoms.positions - reference.positions).flatten())\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Input X contains infinity or a value too large for dtype('float64').", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m model \u001b[38;5;241m=\u001b[39m HarmonicModel(reference\u001b[38;5;241m=\u001b[39mdata[\u001b[38;5;241m0\u001b[39m], regressor\u001b[38;5;241m=\u001b[39mregressor)\n\u001b[0;32m----> 2\u001b[0m hess_model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Packages/faster-molecular-hessians/jitterbug/model/linear.py:75\u001b[0m, in \u001b[0;36mHarmonicModel.train\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 73\u001b[0m y \u001b[38;5;241m=\u001b[39m [atoms\u001b[38;5;241m.\u001b[39mget_potential_energy() \u001b[38;5;241m-\u001b[39m ref_energy \u001b[38;5;28;01mfor\u001b[39;00m atoms \u001b[38;5;129;01min\u001b[39;00m data]\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# Fit the ARD model and ensure it captures the data well\u001b[39;00m\n\u001b[0;32m---> 75\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregressor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfit_intercept\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 76\u001b[0m pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(x)\n\u001b[1;32m 77\u001b[0m max_error \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(pred \u001b[38;5;241m-\u001b[39m y)\u001b[38;5;241m.\u001b[39mmax()\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/linear_model/_base.py:648\u001b[0m, in \u001b[0;36mLinearRegression.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 644\u001b[0m n_jobs_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs\n\u001b[1;32m 646\u001b[0m accept_sparse \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpositive \u001b[38;5;28;01melse\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoo\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 648\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 649\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m 650\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 652\u001b[0m sample_weight \u001b[38;5;241m=\u001b[39m _check_sample_weight(\n\u001b[1;32m 653\u001b[0m sample_weight, X, dtype\u001b[38;5;241m=\u001b[39mX\u001b[38;5;241m.\u001b[39mdtype, only_non_negative\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 654\u001b[0m )\n\u001b[1;32m 656\u001b[0m X, y, X_offset, y_offset, X_scale \u001b[38;5;241m=\u001b[39m _preprocess_data(\n\u001b[1;32m 657\u001b[0m X,\n\u001b[1;32m 658\u001b[0m y,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 661\u001b[0m sample_weight\u001b[38;5;241m=\u001b[39msample_weight,\n\u001b[1;32m 662\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/base.py:584\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m 582\u001b[0m y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[1;32m 583\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 584\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\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[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 585\u001b[0m out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/utils/validation.py:1106\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m 1101\u001b[0m estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n\u001b[1;32m 1102\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1104\u001b[0m )\n\u001b[0;32m-> 1106\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1107\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1108\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1109\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1110\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1111\u001b[0m \u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1112\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1113\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1114\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1115\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1116\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1117\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1118\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1119\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1120\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1122\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[1;32m 1124\u001b[0m check_consistent_length(X, y)\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/utils/validation.py:921\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m 915\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 916\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 917\u001b[0m \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[1;32m 918\u001b[0m )\n\u001b[1;32m 920\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[0;32m--> 921\u001b[0m \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 922\u001b[0m \u001b[43m \u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 923\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 924\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 925\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 926\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_min_samples \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 929\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(array)\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/utils/validation.py:161\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[1;32m 145\u001b[0m \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[1;32m 147\u001b[0m msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 148\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 160\u001b[0m )\n\u001b[0;32m--> 161\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n", - "\u001b[0;31mValueError\u001b[0m: Input X contains infinity or a value too large for dtype('float64')." - ] - } - ], - "source": [ - "model = HarmonicModel(reference=data[0], regressor=regressor)\n", - "hess_model = model.train(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35baa4a3-5e0e-4067-92ea-f29b3c080477", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "print(f'Trained a model with {len(hess_model.coef_)} terms. {(np.abs(hess_model.coef_) > 1e-7).sum()} are nonzero')" - ] - }, - { - "cell_type": "markdown", - "id": "aa509659-701d-4001-8cc7-980c9d999976", - "metadata": {}, - "source": [ - "Compare the forces estimated at a zero displacement to the true value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70d80f87-9983-4bd5-a6ae-b9c966b0d838", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "actual_forces = data[0].get_forces()\n", - "get_model_inputs(data[1], data[0])\n", - "len(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f548b145-0aa8-47f7-802b-6b7232a74bd3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "pred_forces = -hess_model.coef_[:actual_forces.size].reshape((-1, 3))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7cd7762-6e12-4dcd-b564-67a33b18d9e0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "print(f'Maximum force: {np.abs(pred_forces).max():.2e} eV/Angstrom')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "425b77a9-7fd7-40da-af6f-eaed197c9ab6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(1, 2, figsize=(4, 2))\n", - "\n", - "for ax, l, h in zip(axs, ['Actual', 'Estimated'], [actual_forces, pred_forces]):\n", - " ax.matshow(h, vmin=-0.05, vmax=0.05, aspect='auto', cmap='RdBu')\n", - "\n", - " ax.set_xticklabels([])\n", - " ax.set_yticklabels([])\n", - " \n", - " ax.set_title(l, fontsize=10)\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "46a0f2f8-f863-4de3-bd97-97ebd92676d4", - "metadata": {}, - "source": [ - "Get the mean Hessian" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00a10907-667a-413c-851d-d47f0eff092b", - "metadata": {}, - "outputs": [], - "source": [ - "approx_hessian = model.mean_hessian(hess_model)" - ] - }, - { - "cell_type": "markdown", - "id": "f4de2e78-00c2-427f-b9bd-eb3ca881564b", - "metadata": {}, - "source": [ - "Compare to exact answer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d48893fd-df0d-4fa8-bfbe-0d04b71fbf1a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "exact_hess[:3, :3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b311dea-5744-4211-81cb-40aa1183301e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "approx_hessian[:3, :3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "addd7bef-854a-4b9f-96e9-2aa01b652495", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(1, 2, figsize=(4, 2))\n", - "\n", - "for ax, l, h in zip(axs, ['Exact', 'Approximate'], [exact_hess, approx_hessian]):\n", - " ax.matshow(h, vmin=-100, vmax=100, cmap='RdBu')\n", - "\n", - " ax.set_xticklabels([])\n", - " ax.set_yticklabels([])\n", - " \n", - " ax.set_title(l, fontsize=10)\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "b516bb4e-d27d-4ad6-ad4b-b873c81670ff", - "metadata": {}, - "source": [ - "Get the zero point energy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abbbbfd6-7d17-4b93-880a-3352903b56c4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "approx_vibs = VibrationsData.from_2d(data[0], approx_hessian)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdd80af3-8c18-40d8-b971-4a473bc91498", - "metadata": {}, - "outputs": [], - "source": [ - "approx_vibs.get_zero_point_energy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b1af348-4bc9-4ced-9a12-44b3e49abe9c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "exact_zpe" - ] - }, - { - "cell_type": "markdown", - "id": "ab6a6645-bf0e-4ed7-874e-6a345063e0b5", - "metadata": {}, - "source": [ - "The two differ, but I'm not sure how important the difference is." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2ad9116", - "metadata": {}, - "outputs": [], - "source": [ - "freq_diff = [app_freq.real-ex_freq.real for app_freq, ex_freq in zip(approx_vibs.get_frequencies(), exact_freqs)]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b70a0581", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(3.5, 2))\n", - "\n", - "ax.plot(exact_freqs, freq_diff)\n", - "\n", - "ax.set_xlabel('Exact frequency')\n", - "ax.set_ylabel('Frequency difference (cm-1)')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "6489882c-acaf-4a07-bbe9-d643f7c5c882", - "metadata": {}, - "source": [ - "## Plot as a Function of Data\n", - "See what happens as we add more data to the training" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bce41a81-6c88-4b0c-9d8d-0891d1832fd6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "steps = np.linspace(5, len(data), 16, dtype=int)\n", - "print(f'Plotting at {len(steps)} steps: {\", \".join(map(str, steps[:5]))}, ...')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe39ce86-1806-4367-8c86-e3ef58f81f84", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "zpes = []\n", - "freqs = []\n", - "freq_diffs = []\n", - "for count in tqdm(steps):\n", - " with warnings.catch_warnings():\n", - " warnings.simplefilter(\"ignore\")\n", - " hess_model = model.train(data[:count])\n", - " \n", - " approx_hessian = model.mean_hessian(hess_model)\n", - " approx_vibs = VibrationsData.from_2d(data[0], approx_hessian)\n", - " zpes.append(approx_vibs.get_zero_point_energy()*23.060541945329334)\n", - " freqs.append(approx_vibs.get_frequencies())\n", - " freq_diffs.append([app_freq.real-ex_freq.real for app_freq, ex_freq in zip(approx_vibs.get_frequencies(), exact_freqs)])" - ] - }, - { - "cell_type": "markdown", - "id": "c179c3ae-695f-44ad-b548-10002c4ff30b", - "metadata": {}, - "source": [ - "Plot it" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c6706a9-a27f-448f-81d4-957939bb2ca8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(3.5, 2))\n", - "\n", - "ax.plot(steps, zpes)\n", - "\n", - "ax.set_xlim([0, steps.max()])\n", - "ax.plot(ax.get_xlim(), [exact_zpe*23.060541945329334]*2, 'k--')\n", - "\n", - "ax.set_xlabel('Energies')\n", - "ax.set_ylabel('ZPE (eV)')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "e8788f74-c208-4939-aa9b-3bbdfd8310ee", - "metadata": {}, - "source": [ - "We consistently underestimate the ZPE. Is it because we have too few oscillators?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "384af4b3-5eb3-4eac-b176-160f19944853", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pylab as pl\n", - "\n", - "fig, ax = plt.subplots(figsize=(5, 5))\n", - "colors = pl.cm.jet(np.linspace(0,1,len(freq_diffs)))\n", - "\n", - "for i, freq_diff in enumerate(freq_diffs):\n", - " ax.plot(exact_freqs, freq_diff, color=colors[i])\n", - "\n", - "ax.set_xlabel('Exact frequency')\n", - "ax.set_ylabel('Frequency difference (cm-1)')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6cee624f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/proof-of-concept/README.md b/notebooks/proof-of-concept/README.md deleted file mode 100644 index 3ca3018..0000000 --- a/notebooks/proof-of-concept/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Proof of Concept - -The goal behind these notebooks are to illustrate a proof-of-concept for the larger application. diff --git a/notebooks/proof-of-concept/data/approx/butanol_hf_def2-svpd-random-d=5.00e-03.db b/notebooks/proof-of-concept/data/approx/butanol_hf_def2-svpd-random-d=5.00e-03.db deleted file mode 100644 index 3121c51..0000000 Binary files a/notebooks/proof-of-concept/data/approx/butanol_hf_def2-svpd-random-d=5.00e-03.db and /dev/null differ diff --git a/notebooks/proof-of-concept/data/approx/caffeine_hf_def2-svpd-random-d=4.50e-03.db 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file mode 100644 index 5163741..0000000 Binary files a/notebooks/proof-of-concept/data/approx/water_hf_def2-svpd-random-d=8.00e-03.db and /dev/null differ diff --git a/notebooks/proof-of-concept/data/exact/butanol_hf_def2-svpd-ase.json b/notebooks/proof-of-concept/data/exact/butanol_hf_def2-svpd-ase.json deleted file mode 100644 index eb651aa..0000000 --- a/notebooks/proof-of-concept/data/exact/butanol_hf_def2-svpd-ase.json +++ /dev/null @@ -1 +0,0 @@ -{"atoms": {"numbers": {"__ndarray__": [[15], "int64", [8, 6, 6, 6, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}, "positions": {"__ndarray__": [[15, 3], "float64", [-2.460996251803175, 0.3040063296324302, 0.006646659622258883, -0.023189233681202365, 0.35540218284792763, 0.0011872283181692935, 1.2429811459602873, -0.5004207161554253, 0.001998451594288167, -1.2989512550291469, -0.47562768420805446, -0.0238427354357894, 2.5287095316916393, 0.3224161162625796, -0.0016984881105945903, -0.04227505236790298, 0.9997292083914161, 0.8836033789605275, 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} - }, - { - "urn": { - "label": "Diverse Conformer", - "name": "ID List", - "datatype": 6, - "release": "2010.05.05" - }, - "value": { - "ivec": [ - 1 - ] - } - } - ] - } - ], - "props": [ - { - "urn": { - "label": "Charge", - "name": "MMFF94 Partial", - "datatype": 2, - "version": "1.9.0", - "software": "OEChem", - "source": "openeye.com", - "release": "2012.11.26" - }, - "value": { - "slist": [ - "3", - "1 -0.68", - "2 0.28", - "9 0.4" - ] - } - }, - { - "urn": { - "label": "Count", - "name": "Effective Rotor", - "datatype": 7, - "version": "1.7.6", - "software": "OEChem", - "source": "ncbi.nlm.nih.gov", - "release": "2012.01.18" - }, - "value": { - "fval": 0 - } - }, - { - "urn": { - "label": "Features", - "name": "Pharmacophore", - "datatype": 2, - "parameters": "ImplicitMillsDean merged", - "version": "1.8.3", - "software": "OEShape", - "source": "openeye.com", - "release": "2012.11.26" - }, - "value": { - "slist": [ - "2", - "1 1 acceptor", - "1 1 donor" - ] - } - } - ], - "count": { - "heavy_atom": 3, - "atom_chiral": 0, - "atom_chiral_def": 0, - "atom_chiral_undef": 0, - "bond_chiral": 0, - "bond_chiral_def": 0, - "bond_chiral_undef": 0, - "isotope_atom": 0, - "covalent_unit": 1, - "tautomers": 1 - } - } - ] -} diff --git a/notebooks/proof-of-concept/data/structures/water.json b/notebooks/proof-of-concept/data/structures/water.json deleted file mode 100644 index 4c96835..0000000 --- a/notebooks/proof-of-concept/data/structures/water.json +++ /dev/null @@ -1,388 +0,0 @@ -{ - "PC_Compounds": [ - { - "id": { - "id": { - "cid": 962 - } - }, - "atoms": { - "aid": [ - 1, - 2, - 3 - ], - "element": [ - 8, - 1, - 1 - ] - }, - "bonds": { - "aid1": [ - 1, - 1 - ], - "aid2": [ - 2, - 3 - ], - "order": [ - 1, - 1 - ] - }, - "coords": [ - { - "type": [ - 1, - 5, - 255 - ], - "aid": [ - 1, - 2, - 3 - ], - "conformers": [ - { - "x": [ - 2.5369, - 3.0739, - 2 - ], - "y": [ - -0.155, - 0.155, - 0.155 - ] - } - ] - } - ], - "charge": 0, - "props": [ - { - "urn": { - 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"implementation": "E_NROTBONDS", - "version": "3.4.8.18", - "software": "Cactvs", - "source": "Xemistry GmbH", - "release": "2021.10.14" - }, - "value": { - "ival": 0 - } - }, - { - "urn": { - "label": "Fingerprint", - "name": "SubStructure Keys", - "datatype": 16, - "parameters": "extended 2", - "implementation": "E_SCREEN", - "version": "3.4.8.18", - "software": "Cactvs", - "source": "Xemistry GmbH", - "release": "2021.10.14" - }, - "value": { - "binary": "00000371000020000000000000000000000000000000000000000000000000000000000000000000000008000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000" - } - }, - { - "urn": { - "label": "IUPAC Name", - "name": "Allowed", - "datatype": 1, - "version": "2.7.0", - "software": "Lexichem TK", - "source": "OpenEye Scientific Software", - "release": "2021.10.14" - }, - "value": { - "sval": "water" - } - }, - { - "urn": { - "label": "IUPAC Name", - "name": "CAS-like Style", - "datatype": 1, - "version": "2.7.0", - "software": "Lexichem TK", - "source": "OpenEye Scientific Software", - "release": "2021.10.14" - }, - "value": { - "sval": "oxidane" - } - }, - { - "urn": { - "label": "IUPAC Name", - "name": "Markup", - "datatype": 1, - "version": "2.7.0", - "software": "Lexichem TK", - "source": "OpenEye Scientific Software", - "release": "2021.10.14" - }, - "value": { - "sval": "oxidane" - } - }, - { - "urn": { - "label": "IUPAC Name", - "name": "Preferred", - "datatype": 1, - "version": "2.7.0", - "software": "Lexichem TK", - "source": "OpenEye Scientific Software", - "release": "2021.10.14" - }, - "value": { - "sval": "oxidane" - } - }, - { - "urn": { - "label": "IUPAC Name", - "name": "Systematic", - "datatype": 1, - "version": "2.7.0", - "software": "Lexichem TK", - "source": "OpenEye Scientific Software", - "release": "2021.10.14" - }, - "value": { - "sval": "oxidane" - } - }, - { - "urn": { - "label": "IUPAC Name", - "name": "Traditional", - "datatype": 1, - "version": "2.7.0", - "software": "Lexichem TK", - "source": "OpenEye Scientific Software", - "release": "2021.10.14" - }, - "value": { - "sval": "water" - } - }, - { - "urn": { - "label": "InChI", - "name": "Standard", - "datatype": 1, - "version": "1.0.6", - "software": "InChI", - "source": "iupac.org", - "release": "2021.10.14" - }, - "value": { - "sval": "InChI=1S/H2O/h1H2" - } - }, - { - "urn": { - "label": "InChIKey", - "name": "Standard", - "datatype": 1, - "version": "1.0.6", - "software": "InChI", - "source": "iupac.org", - "release": "2021.10.14" - }, - "value": { - "sval": "XLYOFNOQVPJJNP-UHFFFAOYSA-N" - } - }, - { - "urn": { - "label": "Log P", - "name": "XLogP3-AA", - "datatype": 7, - "version": "3.0", - "source": "sioc-ccbg.ac.cn", - "release": "2021.10.14" - }, - "value": { - "fval": -0.5 - } - }, - { - "urn": { - "label": "Mass", - "name": "Exact", - "datatype": 1, - "version": "2.2", - "software": "PubChem", - "source": "ncbi.nlm.nih.gov", - "release": "2021.10.14" - }, - "value": { - "sval": "18.010564683" - } - }, - { - "urn": { - "label": "Molecular Formula", - "datatype": 1, - "version": "2.2", - "software": "PubChem", - "source": "ncbi.nlm.nih.gov", - "release": "2021.10.14" - }, - "value": { - "sval": "H2O" - } - }, - { - "urn": { - "label": "Molecular Weight", - "datatype": 1, - "version": "2.2", - "software": "PubChem", - "source": "ncbi.nlm.nih.gov", - "release": "2021.10.14" - }, - "value": { - "sval": "18.015" - } - }, - { - "urn": { - "label": "SMILES", - "name": "Canonical", - "datatype": 1, - "version": "2.3.0", - "software": "OEChem", - "source": "OpenEye Scientific Software", - "release": "2021.10.14" - }, - "value": { - "sval": "O" - } - }, - { - "urn": { - "label": "SMILES", - "name": "Isomeric", - "datatype": 1, - "version": "2.3.0", - "software": "OEChem", - "source": "OpenEye Scientific Software", - "release": "2021.10.14" - }, - "value": { - "sval": "O" - } - }, - { - "urn": { - "label": "Topological", - "name": "Polar Surface Area", - "datatype": 7, - "implementation": "E_TPSA", - "version": "3.4.8.18", - "software": "Cactvs", - "source": "Xemistry GmbH", - "release": "2021.10.14" - }, - "value": { - "fval": 1 - } - }, - { - "urn": { - "label": "Weight", - "name": "MonoIsotopic", - "datatype": 1, - "version": "2.2", - "software": "PubChem", - "source": "ncbi.nlm.nih.gov", - "release": "2021.10.14" - }, - "value": { - "sval": "18.010564683" - } - } - ], - "count": { - "heavy_atom": 1, - "atom_chiral": 0, - "atom_chiral_def": 0, - "atom_chiral_undef": 0, - "bond_chiral": 0, - "bond_chiral_def": 0, - "bond_chiral_undef": 0, - "isotope_atom": 0, - "covalent_unit": 1, - "tautomers": -1 - } - } - ] -} diff --git a/notebooks/proof-of-concept/sne_0_get-exact-answer.ipynb b/notebooks/proof-of-concept/sne_0_get-exact-answer.ipynb deleted file mode 100644 index 6b3005a..0000000 --- a/notebooks/proof-of-concept/sne_0_get-exact-answer.ipynb +++ /dev/null @@ -1,525 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4bdf2dd5-2160-4343-97c8-c140b028bc6f", - "metadata": {}, - "source": [ - "# Get the Exact Answer\n", - "Start off by computing the exact Hessian to use a reference point. \n", - "First relax the structure then compute the Hessians using [ase's Vibrations module](https://databases.fysik.dtu.dk/ase/ase/vibrations/modes.html), which will compute them numerically using central derivatives" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "06732ce7-a249-448d-8b77-bd7974f83c59", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from ase.thermochemistry import IdealGasThermo\n", - "from ase.vibrations import VibrationsData, Vibrations\n", - "from ase.calculators.psi4 import Psi4\n", - "from ase.optimize import QuasiNewton\n", - "from ase import Atoms, units\n", - "from ase.io import write\n", - "from time import perf_counter\n", - "from platform import node\n", - "from pathlib import Path\n", - "import numpy as np\n", - "import shutil\n", - "import json\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "id": "70e35fde-8d07-46e2-86e7-642a201a8c4a", - "metadata": {}, - "source": [ - "Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "1abbc967-a788-4a2d-8342-6a88c75cebec", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "molecule_name = 'ethanol'\n", - "method = 'wb97x-d'\n", - "basis = 'cc-pvtz'\n", - "threads = min(os.cpu_count(), 12)" - ] - }, - { - "cell_type": "markdown", - "id": "748e04a6-dd84-4142-aa0d-7bd543346d79", - "metadata": {}, - "source": [ - "Derived" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "aeebbc77-70e4-4709-90a0-b9aaf54d4cd9", - "metadata": {}, - "outputs": [], - "source": [ - "run_name = f'{molecule_name}_{method}_{basis}'" - ] - }, - { - "cell_type": "markdown", - "id": "026bbc46-d5e6-4b67-a9a4-aa0cd86f9ad2", - "metadata": {}, - "source": [ - "## Load in Target Molecule\n", - "We have it in a JSON file from PubChem" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "d52bd814-a353-467f-99a6-02201a64416e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def load_molecule(name: str) -> Atoms:\n", - " \"\"\"Load a molecule from a PubChem JSON file\n", - " \n", - " Args:\n", - " name: Name of the molecule\n", - " Returns:\n", - " ASE Atoms object\n", - " \"\"\"\n", - " \n", - " # Get the compound data\n", - " with open(f'data/structures/{name}.json') as fp:\n", - " data = json.load(fp)\n", - " data = data['PC_Compounds'][0]\n", - " \n", - " # Extract data from the JSON\n", - " atomic_numbers = data['atoms']['element']\n", - " positions = np.zeros((len(atomic_numbers), 3))\n", - " conf_data = data['coords'][0]['conformers'][0]\n", - " for i, c in enumerate('xyz'):\n", - " if c in conf_data:\n", - " positions[:, i] = conf_data[c]\n", - " \n", - " # Build the object \n", - " return Atoms(numbers=atomic_numbers, positions=positions)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "02010c51-39a9-42e6-a4c8-f03d447267ff", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "atoms = load_molecule(molecule_name)" - ] - }, - { - "cell_type": "markdown", - "id": "9a42b230-c0c2-4512-9afb-57cea203c96e", - "metadata": {}, - "source": [ - "## Perform the Geometry Optimization\n", - "Build the ASE calculator then run QuasiNewton to a high tolerance" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "7073bdd7-9ee6-45bf-a401-93cfe122413a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "calc = Psi4(method=method, basis=basis, num_threads=threads, memory='4096MB')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "ef903a43-5d6c-47fb-a500-837599c95f91", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Step[ FC] Time Energy fmax\n", - "BFGSLineSearch: 0[ 0] 14:30:14 -4219.144408 0.7930\n", - "BFGSLineSearch: 1[ 2] 14:30:31 -4219.158073 0.5048\n", - "BFGSLineSearch: 2[ 4] 14:30:49 -4219.168016 0.3333\n", - "BFGSLineSearch: 3[ 6] 14:31:07 -4219.173063 0.1898\n", - "BFGSLineSearch: 4[ 8] 14:31:24 -4219.176127 0.2506\n", - "BFGSLineSearch: 5[ 10] 14:31:42 -4219.180164 0.1618\n", - "BFGSLineSearch: 6[ 12] 14:31:59 -4219.181108 0.0880\n", - "BFGSLineSearch: 7[ 14] 14:32:16 -4219.181824 0.1177\n", - "BFGSLineSearch: 8[ 16] 14:32:33 -4219.182404 0.0521\n", - "BFGSLineSearch: 9[ 17] 14:32:42 -4219.182678 0.0919\n", - "BFGSLineSearch: 10[ 19] 14:32:59 -4219.182811 0.0259\n", - "BFGSLineSearch: 11[ 20] 14:33:08 -4219.182914 0.0207\n", - "BFGSLineSearch: 12[ 22] 14:33:26 -4219.182940 0.0211\n", - "BFGSLineSearch: 13[ 24] 14:33:43 -4219.182970 0.0185\n", - "BFGSLineSearch: 14[ 25] 14:33:52 -4219.183027 0.0181\n", - "BFGSLineSearch: 15[ 28] 14:34:18 -4219.183230 0.0440\n", - "BFGSLineSearch: 16[ 29] 14:34:27 -4219.183314 0.0179\n", - "BFGSLineSearch: 17[ 30] 14:34:35 -4219.183355 0.0054\n", - "CPU times: user 47min 18s, sys: 1min 28s, total: 48min 47s\n", - "Wall time: 4min 32s\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "atoms.calc = calc\n", - "dyn = QuasiNewton(atoms)\n", - "dyn.run(fmax=0.01)" - ] - }, - { - "cell_type": "markdown", - "id": "bce1a800-943a-4627-b383-eff82e43a456", - "metadata": {}, - "source": [ - "Save the output file" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "a695054d-a768-466e-9771-54395a3c2b81", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "out_dir = Path('data') / 'exact'\n", - "out_dir.mkdir(exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "16317d90-cb1d-4347-9eca-4ba3123bdf4b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "write(out_dir / f'{run_name}.xyz', atoms)" - ] - }, - { - "cell_type": "markdown", - "id": "da5b9bdc-a0b1-4c0e-b2d9-42f79dfbac6a", - "metadata": {}, - "source": [ - "## Compute the Hessian using ASE\n", - "ASE has a built-in method which uses finite displacements" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "857e38e3-8b92-4246-8469-5ce6f381d56b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "if Path('vib').is_dir():\n", - " shutil.rmtree('vib')" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "73b9b8bb-1d32-4e29-9380-95002bb1081e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 53min 15s, sys: 1min 35s, total: 54min 51s\n", - "Wall time: 5min 6s\n" - ] - } - ], - "source": [ - "%%time\n", - "finite_diff_time = perf_counter()\n", - "vib = Vibrations(atoms)\n", - "vib.run()\n", - "finite_diff_time = perf_counter() - finite_diff_time" - ] - }, - { - "cell_type": "markdown", - "id": "497a7013-9ac6-484f-a5e3-d19e11224f4a", - "metadata": {}, - "source": [ - "Save the vibration data" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "588b0344-ad43-4a6b-b57b-03782327f1e7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "vib_data = vib.get_vibrations()\n", - "with (out_dir / f'{run_name}-ase.json').open('w') as fp:\n", - " vib_data.write(fp)" - ] - }, - { - "cell_type": "markdown", - "id": "4711060e-aaee-4d64-ac26-0833965703ad", - "metadata": {}, - "source": [ - "Print the ZPE for reference" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "450b9a10-5c0b-434a-8b20-fedf5f1b1f48", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.1884190925383207" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vib_data.get_zero_point_energy()" - ] - }, - { - "cell_type": "markdown", - "id": "47b7eea7-b7c6-4765-961d-7ebaf7332fe6", - "metadata": {}, - "source": [ - "## Repeat with Psi4's analytic derivatives\n", - "See if we get the same answer faster" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "577cd427-7829-4f7b-8457-a6c709c3ea80", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "analytic_time = perf_counter()\n", - "calc.set_psi4(atoms)\n", - "hess = calc.psi4.hessian(f'{method}/{basis}')\n", - "analytic_time = perf_counter() - analytic_time" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "0d611176-e94d-44de-a57b-6750625463a1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "analytic_hess = hess.to_array() * units.Hartree / units.Bohr / units.Bohr" - ] - }, - { - "cell_type": "markdown", - "id": "9567ec44-fda4-4052-87dc-c6c257adbc4e", - "metadata": {}, - "source": [ - "Convert it to an ASE object and save" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "8a767054-2b20-4a87-8f51-5e715d71d539", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "vib_data = VibrationsData.from_2d(atoms, analytic_hess)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "b5053fa4-e301-472a-8982-1b02c7727d90", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "vib_data = vib.get_vibrations()\n", - "with (out_dir / f'{run_name}-psi4.json').open('w') as fp:\n", - " vib_data.write(fp)" - ] - }, - { - "cell_type": "markdown", - "id": "36d8ad22-2ad6-45c0-ba9d-0aeed39cb41f", - "metadata": {}, - "source": [ - "Print the ZPE for reference" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "9e9422b7-a62d-49b7-af4a-b433d24804ae", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.1884190925383207" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vib_data.get_zero_point_energy()" - ] - }, - { - "cell_type": "markdown", - "id": "74673a35-e2c9-43ac-a261-be03dfc61208", - "metadata": {}, - "source": [ - "Save the runtimes" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "273b5116-06ec-4098-8b20-537d1f9d7e84", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "with (out_dir / f'{run_name}-times.json').open('w') as fp:\n", - " json.dump({\n", - " 'hostname': node(),\n", - " 'finite-diff': finite_diff_time,\n", - " 'analytic': analytic_time,\n", - " }, fp)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "b24037ee-0ecc-4616-9fa5-f5941fb049d9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "306.2348163090646\n", - "300.2469820249826\n" - ] - } - ], - "source": [ - "print(finite_diff_time)\n", - "print(analytic_time)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04dfcdeb", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b204a441", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/proof-of-concept/sne_1_compute-random-offsets.ipynb b/notebooks/proof-of-concept/sne_1_compute-random-offsets.ipynb deleted file mode 100644 index 22b5d4e..0000000 --- a/notebooks/proof-of-concept/sne_1_compute-random-offsets.ipynb +++ /dev/null @@ -1,361 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8233267b-e98b-44be-b9aa-116d0e67a94b", - "metadata": {}, - "source": [ - "# Compute Energies of Random Offsets\n", - "Form a training set for approximate hessians by computing energies at many random displacements." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c6a28419-6831-4197-8973-00c5591e19cb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from ase.calculators.psi4 import Psi4\n", - "from ase.io import write, read\n", - "from ase.db import connect\n", - "from ase import Atoms\n", - "from pathlib import Path\n", - "from tqdm import tqdm \n", - "import numpy as np\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "id": "cec456a7-3c13-4b00-936a-abc31c898262", - "metadata": {}, - "source": [ - "Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c6be56c5-a460-4acd-9b89-8c3d9c812f5f", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "molecule_name = 'ethanol'\n", - "method = 'b3lyp'\n", - "basis = '6-31g*'\n", - "method = 'wb97x-d'\n", - "basis = 'cc-pvtz'\n", - "threads = min(os.cpu_count(), 12)\n", - "step_size: float = 0.005 # Perturbation amount, used as maximum L2 norm" - ] - }, - { - "cell_type": "markdown", - "id": "7010df09-73b2-4d58-be03-15a5f0d04b4c", - "metadata": {}, - "source": [ - "Derived" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0b6794cd-477f-45a1-b96f-2332804ddb20", - "metadata": {}, - "outputs": [], - "source": [ - "run_name = f'{molecule_name}_{method}_{basis}'" - ] - }, - { - "cell_type": "markdown", - "id": "cf9ff792-6b5b-46ce-9a78-78912e372912", - "metadata": {}, - "source": [ - "## Load in the Relaxed Structure\n", - "We generated a relaxed structure in the previous notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ad9fd725-b1ba-4fec-ae41-959be0e540b3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Atoms(symbols='OC2H6', pbc=False, forces=..., calculator=SinglePointCalculator(...))" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "atoms = read(Path('data') / 'exact' / f'{run_name}.xyz')\n", - "atoms" - ] - }, - { - "cell_type": "markdown", - "id": "2284056b-ddf2-4a3b-88ca-b1c6dc84a2d5", - "metadata": {}, - "source": [ - "## Compute many random energies\n", - "Compute $3N + 3N(3N+1)/2 + 1$ energies with displacements sampled [on the unit sphere](https://mathoverflow.net/questions/24688/efficiently-sampling-points-uniformly-from-the-surface-of-an-n-sphere). This is enough to fit the Jacobian and Hessian exactly plus a little more" - ] - }, - { - "cell_type": "markdown", - "id": "ad4c5d8e-96d4-4bb6-9bf2-6474d7563448", - "metadata": {}, - "source": [ - "Prepare the output directory" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "23502eea-0974-4248-8f19-e85447069c61", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "out_dir = Path('data') / 'approx'\n", - "out_dir.mkdir(exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "bf1366fc-d9a7-4a98-b9c9-cb3a0209b406", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "db_path = out_dir / f'{run_name}-random-d={step_size:.2e}.db'" - ] - }, - { - "cell_type": "markdown", - "id": "004158dc-3fe9-47a6-99dd-268aa69bb27b", - "metadata": {}, - "source": [ - "Add the relaxed geometry if needed" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d4f21e81-5ec3-4877-a4d1-402077be2ee8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "if not db_path.is_file():\n", - " with connect(db_path) as db:\n", - " db.write(atoms)" - ] - }, - { - "cell_type": "markdown", - "id": "56ebf431-75a0-44d5-8e18-43f2898d6dab", - "metadata": {}, - "source": [ - "Make the calculator" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0915595d-133a-43df-84fc-4ff6a3b538ea", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " Memory set to 3.815 GiB by Python driver.\n", - " Threads set to 12 by Python driver.\n" - ] - } - ], - "source": [ - "calc = Psi4(method=method, basis=basis, num_threads=threads, memory='4096MB')" - ] - }, - { - "cell_type": "markdown", - "id": "8e9e5ff2-3728-459b-b3d3-09acba0f71bc", - "metadata": {}, - "source": [ - "Generate the energies" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e2a28593-2634-4bb7-ae5b-8f557937bda1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Need to run 406 calculations for full accuracy.\n" - ] - } - ], - "source": [ - "n_atoms = len(atoms)\n", - "to_compute = 3 * n_atoms + 3 * n_atoms * (3 * n_atoms + 1) // 2 + 1\n", - "#to_compute *= 2\n", - "print(f'Need to run {to_compute} calculations for full accuracy.')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8bf40523-dcaa-4046-a9c6-74e35178e87f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Already done 58. 348 left to do.\n" - ] - } - ], - "source": [ - "with connect(db_path) as db:\n", - " done = len(db)\n", - "print(f'Already done {done}. {to_compute - done} left to do.')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a6fa1b33-defc-4b35-895d-052eb64453fb", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 0%| | 0/406 [00:00 1e-7).sum()} are nonzero')" - ] - }, - { - "cell_type": "markdown", - "id": "aa509659-701d-4001-8cc7-980c9d999976", - "metadata": {}, - "source": [ - "Compare the forces estimated at a zero displacement to the true value" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "id": "70d80f87-9983-4bd5-a6ae-b9c966b0d838", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "actual_forces = data[0].get_forces()\n", - "n_atoms = len(data[0])\n", - "to_compute = 3 * n_atoms + 3 * n_atoms * (3 * n_atoms + 1) // 2 + 1\n", - "# ohdist = [datum.get_distance(0,1)- data[0].get_distance(0,1) for datum in data]\n", - "# ohdist2 = [datum.get_distance(0,2)- data[0].get_distance(0,2) for datum in data]\n", - "\n", - "# enes = [23.06*(datum.get_potential_energy() - data[0].get_potential_energy()) for datum in data]\n", - "# pred_enes = [23.06 * ene for ene in hess_model.predict([get_model_internal_inputs(atoms, data[0]) for atoms in data])]\n", - "# fig, ax = plt.subplots(figsize=(5,5))\n", - "# ax = plt.axes(projection='3d')\n", - "# ax.scatter3D(ohdist, ohdist2, enes)\n", - "\n", - "\n", - "# fig, ax = plt.subplots(figsize=(5,5))\n", - "# ax = plt.axes(projection='3d')\n", - "# ax.scatter3D(ohdist, ohdist2, pred_enes)\n", - "\n", - "\n", - "\n", - "# print(max([pred_ene - ene for pred_ene, ene in zip(pred_enes, enes)]))" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "id": "fef38b82", - "metadata": {}, - "outputs": [], - "source": [ - "# fig, ax = plt.subplots(figsize=(5,5))\n", - "# ax.scatter(ohdist2, enes)\n", - "# pred_inp = []\n", - "# new_pred_ohdists = []\n", - "# for i in range(50):\n", - "# disp = np.random.normal(0, 1, size=(len(data[0]) * 3))\n", - "# disp /= np.linalg.norm(disp)\n", - "# disp *= step_size#*random.randint(20,100)/100\n", - "# disp = disp.reshape((-1, 3))\n", - " \n", - "# # Subtract off any translation\n", - "# disp -= disp.mean(axis=0)[None, :]\n", - "\n", - "# # Make the new atoms\n", - "# new_atoms = data[0].copy()\n", - "# new_atoms.positions += disp\n", - "# pred_inp.append(get_model_internal_inputs(new_atoms, data[0]))\n", - "# new_pred_ohdists.append(new_atoms.get_distance(0,1)- data[0].get_distance(0,1))\n", - "# new_pred_enes = hess_model.predict(pred_inp)\n", - "# new_pred_enes = [23.06 * ene for ene in new_pred_enes]\n", - "# ax.scatter(new_pred_ohdists, new_pred_enes)\n", - "# ax.set_xlabel('O-H displacement')\n", - "# ax.set_ylabel('energy')" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "id": "f548b145-0aa8-47f7-802b-6b7232a74bd3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "pred_forces = -hess_model.coef_[:actual_forces.size].reshape((-1, 3))" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "id": "d7cd7762-6e12-4dcd-b564-67a33b18d9e0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Maximum force: 7.72e+00 eV/Angstrom\n" - ] - } - ], - "source": [ - "print(f'Maximum force: {np.abs(pred_forces).max():.2e} eV/Angstrom')" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "id": "425b77a9-7fd7-40da-af6f-eaed197c9ab6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1, 2, figsize=(4, 2))\n", - "\n", - "for ax, l, h in zip(axs, ['Actual', 'Estimated'], [actual_forces, pred_forces]):\n", - " ax.matshow(h, vmin=-0.05, vmax=0.05, aspect='auto', cmap='RdBu')\n", - "\n", - " ax.set_xticklabels([])\n", - " ax.set_yticklabels([])\n", - " \n", - " ax.set_title(l, fontsize=10)\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "46a0f2f8-f863-4de3-bd97-97ebd92676d4", - "metadata": {}, - "source": [ - "Get the mean Hessian" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "id": "00a10907-667a-413c-851d-d47f0eff092b", - "metadata": {}, - "outputs": [], - "source": [ - "approx_hessian = model.mean_hessian(hess_model, data[0])" - ] - }, - { - "cell_type": "markdown", - "id": "f4de2e78-00c2-427f-b9bd-eb3ca881564b", - "metadata": {}, - "source": [ - "Compare to exact answer" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "id": "d48893fd-df0d-4fa8-bfbe-0d04b71fbf1a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 2.69016123e+01, -1.28458641e+01, 3.36727325e+00,\n", - " -1.75307484e+01, 6.39414201e+00, -2.70410023e+00,\n", - " -4.19781009e+00, 3.00911307e+00, 1.25621745e-01,\n", - " -1.14264833e+00, 1.42416326e+00, -4.30216652e-01,\n", - " -5.63190896e-01, 7.06647583e-01, 3.20462967e-01,\n", - " -7.98375929e-01, -5.05108226e-01, -1.69079269e-02,\n", - " 3.14585947e-01, 8.25866188e-02, 6.18862724e-02,\n", - " 3.16352059e-01, 7.63867259e-02, -1.20350918e-01,\n", - " -3.29609746e+00, 1.67639180e+00, -6.03636525e-01],\n", - " [-1.28458641e+01, 3.22899398e+01, 2.49678233e+01,\n", - " 9.11507477e+00, -1.27669506e+01, 2.98634995e-01,\n", - " -5.71854182e-01, 1.02078058e+00, -1.90378639e-01,\n", - " 2.81137569e+00, -1.75327349e+00, 1.13238325e-01,\n", - " 2.46104075e+00, -1.69009254e+00, -1.50141065e-01,\n", - " -3.27459074e-01, 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-1.61602256e+00,\n", - " -1.58918880e+00, -1.21420425e+00, 3.59796759e-02,\n", - " -1.11583260e-01, 7.42074191e-02, -4.28426028e-02,\n", - " -2.99872491e-01, 4.46451359e-02, 1.52649975e-01,\n", - " -4.48235900e-01, 1.90501204e-02, -4.55277392e-01],\n", - " [ 6.39414201e+00, -1.27669506e+01, -1.98068769e+00,\n", - " 6.06323568e-01, 5.41203042e+01, 2.22794186e+00,\n", - " -5.54288021e+00, -1.25534889e+01, 1.11221291e-01,\n", - " -7.96241751e-01, -1.32991429e+01, 1.10673989e+01,\n", - " -1.67180353e+00, -1.23505604e+01, -1.17349069e+01,\n", - " 2.15649068e+00, 1.22927248e+00, -3.06495800e-02,\n", - " -1.93079328e+00, -1.33115018e+00, 8.67653924e-03,\n", - " -1.73411754e+00, -1.24188859e+00, 6.69828073e-02,\n", - " 2.51786213e+00, -1.75631844e+00, 2.44340641e-01],\n", - " [-2.70410023e+00, 2.98634995e-01, -8.77947105e+00,\n", - " 4.70677819e-01, 2.22794186e+00, 6.11177702e+01,\n", - " 2.67839483e-01, -1.35967694e-02, -8.39246670e+00,\n", - " 1.00360538e+00, 1.07601787e+01, 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"tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(1, 2, figsize=(4, 2))\n", - "\n", - "for ax, l, h in zip(axs, ['Exact', 'Approximate'], [exact_hess, approx_hessian]):\n", - " ax.matshow(h, vmin=-100, vmax=100, cmap='RdBu')\n", - "\n", - " ax.set_xticklabels([])\n", - " ax.set_yticklabels([])\n", - " \n", - " ax.set_title(l, fontsize=10)\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "b516bb4e-d27d-4ad6-ad4b-b873c81670ff", - "metadata": {}, - "source": [ - "Get the zero point energy" - ] - }, - { - "cell_type": "code", - "execution_count": 169, - "id": "abbbbfd6-7d17-4b93-880a-3352903b56c4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-3.89983883e-04 -1.71867556e-04 -7.11394971e-15 5.82848976e-15\n", - " 1.34448773e-14 7.99332376e-04 8.04244488e-01 1.45362418e+00\n", - " 2.43120869e+00 3.08677324e+00 4.06079173e+00 5.92410416e+00\n", - " 7.23141473e+00 1.14192054e+01 1.26521469e+01 1.66491206e+01\n", - " 1.73611874e+01 1.75412399e+01 1.90691724e+01 2.45390967e+01\n", - " 4.23310191e+01 5.51917811e+01 5.95863497e+01 6.11185120e+01\n", - " 6.18334255e+01 6.26709710e+01 8.90542797e+01]\n", - "[-1.55611349e-02 -4.26253757e-03 -2.19885489e-03 -7.28129306e-04\n", - " -5.16033228e-04 1.82275255e-04 2.43419510e-01 4.48980314e-01\n", - " 2.30045175e+00 3.02202121e+00 5.09296039e+00 6.52143862e+00\n", - " 8.10341616e+00 8.87163034e+00 1.07894181e+01 1.17285537e+01\n", - " 1.48030541e+01 1.73621694e+01 2.01483364e+01 2.78245586e+01\n", - " 5.03103274e+01 6.65487612e+01 7.56938035e+01 7.85362795e+01\n", - " 8.40871890e+01 8.81908576e+01 1.12628340e+02]\n", - "[0.00000000e+00+42.52946942j 0.00000000e+00+23.69312092j\n", - " 0.00000000e+00+11.70107322j 0.00000000e+00 +6.20558433j\n", - " 0.00000000e+00 +5.48588273j 3.05313884e+00 +0.j\n", - " 2.40827866e+02 +0.j 2.76991090e+02 +0.j\n", - " 4.27569983e+02 +0.j 8.07573462e+02 +0.j\n", - " 9.00310641e+02 +0.j 1.06891110e+03 +0.j\n", - " 1.10316712e+03 +0.j 1.15052529e+03 +0.j\n", - " 1.28753031e+03 +0.j 1.38394554e+03 +0.j\n", - " 1.40767226e+03 +0.j 1.43424390e+03 +0.j\n", - " 1.49002436e+03 +0.j 1.50277290e+03 +0.j\n", - " 1.52405972e+03 +0.j 3.00868959e+03 +0.j\n", - " 3.04065374e+03 +0.j 3.09278343e+03 +0.j\n", - " 3.11684372e+03 +0.j 3.13183922e+03 +0.j\n", - " 3.90159261e+03 +0.j ]\n", - "[0.00000000e+00+5.99219247e+00j 0.00000000e+00+4.35040394e+00j\n", - " 0.00000000e+00+3.91695895e-05j 1.54810198e-05+0.00000000e+00j\n", - " 2.10022159e-05+0.00000000e+00j 8.53812609e+00+0.00000000e+00j\n", - " 2.70106221e+02+0.00000000e+00j 5.77376507e+02+0.00000000e+00j\n", - " 7.43528352e+02+0.00000000e+00j 7.68657930e+02+0.00000000e+00j\n", - " 8.30388935e+02+0.00000000e+00j 9.29450687e+02+0.00000000e+00j\n", - " 9.60579407e+02+0.00000000e+00j 9.83613082e+02+0.00000000e+00j\n", - " 1.10775442e+03+0.00000000e+00j 1.29818394e+03+0.00000000e+00j\n", - " 1.41282891e+03+0.00000000e+00j 1.59376074e+03+0.00000000e+00j\n", - " 1.74254927e+03+0.00000000e+00j 1.79964717e+03+0.00000000e+00j\n", - " 1.88589962e+03+0.00000000e+00j 2.66032284e+03+0.00000000e+00j\n", - " 2.72018606e+03+0.00000000e+00j 2.74115581e+03+0.00000000e+00j\n", - " 2.74538618e+03+0.00000000e+00j 2.79871506e+03+0.00000000e+00j\n", - " 3.41487724e+03+0.00000000e+00j]\n" - ] - } - ], - "source": [ - "approx_vibs = VibrationsData.from_2d(data[0], approx_hessian)\n", - "print(np.linalg.eigh(approx_hessian)[0])\n", - "print(np.linalg.eigh(exact_hess)[0])\n", - "print(exact_freqs)\n", - "print(approx_vibs.get_frequencies())" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "id": "fdd80af3-8c18-40d8-b971-4a473bc91498", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.107328812193435" - ] - }, - "execution_count": 170, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "approx_vibs.get_zero_point_energy()" - ] - }, - { - "cell_type": "code", - "execution_count": 171, - "id": "6b1af348-4bc9-4ced-9a12-44b3e49abe9c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.1884190925383207" - ] - }, - "execution_count": 171, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exact_zpe" - ] - }, - { - "cell_type": "markdown", - "id": "ab6a6645-bf0e-4ed7-874e-6a345063e0b5", - "metadata": {}, - "source": [ - "The two differ, but I'm not sure how important the difference is." - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "id": "b2ad9116", - "metadata": {}, - "outputs": [], - "source": [ - "freq_diff = [app_freq.real-ex_freq.real for app_freq, ex_freq in zip(approx_vibs.get_frequencies(), exact_freqs)]" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "id": "b70a0581", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/lcrc/project/PACC/conda/elliott/miniconda3/envs/jitterbug/lib/python3.10/site-packages/matplotlib/cbook/__init__.py:1335: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(3.5, 2))\n", - "\n", - "ax.plot(exact_freqs, freq_diff)\n", - "\n", - "ax.set_xlabel('Exact frequency')\n", - "ax.set_ylabel('Frequency difference (cm-1)')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "6489882c-acaf-4a07-bbe9-d643f7c5c882", - "metadata": {}, - "source": [ - "## Plot as a Function of Data\n", - "See what happens as we add more data to the training" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "id": "bce41a81-6c88-4b0c-9d8d-0891d1832fd6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting at 39 steps: 5, 15, 25, 35, 45, ...\n" - ] - } - ], - "source": [ - "interval = 10\n", - "steps = np.linspace(5, len(data), int(len(data)/interval), dtype=int)\n", - "print(f'Plotting at {len(steps)} steps: {\", \".join(map(str, steps[:5]))}, ...')" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "id": "fe39ce86-1806-4367-8c86-e3ef58f81f84", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting at 19 steps: 50, 69, 88, 107, 126, ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 19/19 [00:32<00:00, 1.69s/it]\n" - ] - } - ], - "source": [ - "all_zpes = []\n", - "all_freqs = []\n", - "all_freq_diffs = []\n", - "all_steps = []\n", - "\n", - "step_sizes = [0.005]\n", - "step_sizes = [0.002, 0.004, .0045, 0.005, 0.0055, .0056, .0057, 0.006, .008, 0.01, .04]\n", - "step_sizes = [.002, 0.0045, 0.005, 0.01]\n", - "step_sizes = [0.005]\n", - "interval = 20\n", - "#for step_size in [0.005]:\n", - "#for step_size in [0.002, 0.004, .0045, 0.005, 0.0055, .0056, .0057, 0.006, .008, 0.01, .04]:\n", - "for step_size in step_sizes:\n", - " zpes = []\n", - " freqs = []\n", - " freq_diffs = []\n", - " db_path = out_dir / f'{run_name}-random-d={step_size:.2e}.db'\n", - " with connect(db_path) as db:\n", - " data = [a.toatoms() for a in db.select('')]\n", - " steps = np.linspace(50, len(data), int(len(data)/interval), dtype=int)\n", - " print(f'Plotting at {len(steps)} steps: {\", \".join(map(str, steps[:5]))}, ...')\n", - " for count in tqdm(steps):\n", - " with warnings.catch_warnings():\n", - " warnings.simplefilter(\"ignore\")\n", - " hess_model = model.train(data[:count])\n", - " approx_hessian = model.mean_hessian(hess_model, data[0])\n", - " approx_vibs = VibrationsData.from_2d(data[0], approx_hessian)\n", - " zpes.append(approx_vibs.get_zero_point_energy()*23.060541945329334)\n", - " freqs.append(approx_vibs.get_frequencies())\n", - " freq_diffs.append([app_freq.real-ex_freq.real for app_freq, ex_freq in zip(approx_vibs.get_frequencies(), exact_freqs)])\n", - " all_zpes.append(zpes)\n", - " all_freqs.append(freqs)\n", - " all_freq_diffs.append(freq_diffs)\n", - " all_steps.append(steps)" - ] - }, - { - "cell_type": "markdown", - "id": "c179c3ae-695f-44ad-b548-10002c4ff30b", - "metadata": {}, - "source": [ - "Plot it" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "id": "1c6706a9-a27f-448f-81d4-957939bb2ca8", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pylab as pl\n", - "max_steps = 0 \n", - "fig, ax = plt.subplots(figsize=(8, 3))\n", - "for zpes, sizes, steps in zip(all_zpes, step_sizes, all_steps):\n", - " \n", - " if steps[-1] > max_steps:\n", - " max_steps = steps[-1]\n", - " ax.plot(steps, zpes, label='stepsize: {:.2e}'.format(sizes))\n", - "\n", - "ax.set_xlim([0, 3000])\n", - "ax.plot(ax.get_xlim(), [exact_zpe*23.060541945329334]*2, 'k--')\n", - "plt.axvline(x=to_compute)\n", - "ax.set_ylim([0,exact_zpe*23.060541945329334*2])\n", - "ax.set_xlabel('Energies')\n", - "ax.set_ylabel('ZPE (kcal/mol)')\n", - "ax.legend()\n", - "fig.tight_layout()\n" - ] - }, - { - "cell_type": "markdown", - "id": "e8788f74-c208-4939-aa9b-3bbdfd8310ee", - "metadata": {}, - "source": [ - "We consistently underestimate the ZPE. Is it because we have too few oscillators?" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "384af4b3-5eb3-4eac-b176-160f19944853", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/lcrc/project/PACC/conda/elliott/miniconda3/envs/jitterbug/lib/python3.10/site-packages/matplotlib/collections.py:192: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " offsets = np.asanyarray(offsets, float)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pylab as pl\n", - "import matplotlib\n", - "for freq_diffs, sizes, steps in zip(all_freq_diffs, step_sizes, all_steps):\n", - " start=0\n", - " end=200\n", - " fig, axs = plt.subplots(1, 2, figsize=(8, 4), gridspec_kw={'width_ratios': [20, 1]})\n", - " colors = pl.cm.jet(np.linspace(0,1,len(freq_diffs[start:end])))\n", - "\n", - " for i, freq_diff in enumerate(freq_diffs[start:end]):\n", - " plot = axs[0].scatter(exact_freqs, freq_diff, color=colors[i], marker='o', facecolor='none') \n", - " axs[0].set_xlabel('Exact frequency')\n", - " axs[0].set_ylabel('Frequency difference (cm-1)')\n", - " axs[0].set_title('Step size: {:.2e}'.format(sizes))\n", - " cmap = matplotlib.cm.jet\n", - " norm = matplotlib.colors.Normalize(vmin=steps[start], vmax=steps[i])\n", - "\n", - " cb1 = matplotlib.colorbar.ColorbarBase(axs[1], cmap=cmap,\n", - " norm=norm,\n", - " orientation='vertical')\n", - " cb1.set_label('number of energies', rotation=270)\n", - " fig.tight_layout()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "id": "6cee624f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Step sizes 0.005\n", - "Max Deviation (cm-1) 361.83485295182095\n", - "Mean Absolute Deviation (cm-1) 176.36908124015793\n", - "Mean Deviation (cm-1) -48.42908414826238\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - " for freq_diffs, sizes, steps in zip(all_freq_diffs, step_sizes, all_steps):\n", - " fig, ax = plt.subplots(figsize=(10, 5))\n", - "\n", - " ax.boxplot(freq_diffs)\n", - " for i, val in enumerate(steps):\n", - " if val > to_compute:\n", - " break\n", - " plt.axvline(x=i+1)\n", - " fig.tight_layout()\n", - " ax.set_xticklabels(steps)\n", - " ax.set_xlabel('Number of energies')\n", - " ax.set_ylabel('Frequency difference (cm-1)')\n", - " print(\"Step sizes\", sizes)\n", - " print(\"Max Deviation (cm-1)\", max(freq_diffs[-1]))\n", - " print(\"Mean Absolute Deviation (cm-1)\", sum([abs(freq) for freq in freq_diffs[-1]])/len(freq_diffs[-1]))\n", - " print(\"Mean Deviation (cm-1)\", sum(freq_diffs[-1])/len(freq_diffs[-1]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c64ae17b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}