{ "cells": [ { "cell_type": "markdown", "id": "35d79007", "metadata": {}, "source": [ "# Training SchNet on rMD17 (ethanol)\n", "\n", "This notebook trains the `xnn` **SchNet** from scratch on the paper's own\n", "benchmark task: predicting the **energies and forces** of a molecular-dynamics\n", "trajectory of a small organic molecule, ethaol. The dataset is downloaded in\n", "seconds through the `xnn` hub and the model reproduces the [SchNet\n", "manuscript](https://arxiv.org/pdf/1706.08566)'s headline at small scale:\n", "\n", "* Training on joint **energy + force training** with the paper's loss weighting\n", " ($\\rho = 0.01$): forces are calculated via autograd through the\n", " `ForceStressOutput` wrapper the `Trainer` adds (Thus, the model is\n", " energy-conserving by construction).\n", "* The trainer's **loggers**: every epoch's losses and MAE/RMSE, averaged over\n", " the whole training and validation sets, go to `metrics.csv` which is used to\n", " draw the learning curves.\n", "* An **energy/force parity** plot vs DFT labels on held-out conformations is created.\n", "* A **smooth 1-D potential-energy scan**, the property that continuous filters +\n", " shifted softplus can calculate over discretized approaches.\n", "\n", "The model implemented here is based on the paper's architecture ($F = 64$, $T =\n", "3$, Gaussian RBFs every 0.1 Å with $\\gamma = 10$ Å$^{-2}$). The implementation\n", "is verified against the manuscripts' equations block by block in\n", "`examples/fidelity_checks/schnet_verification.ipynb`." ] }, { "cell_type": "markdown", "id": "b8192299", "metadata": {}, "source": [ "
\n", "\n", "Note: Run with this notebook with xnn kernel (which uses the GPU\n", "when available). See the documentation or pyproject.toml for installation\n", "instructions.\n", "
" ] }, { "cell_type": "markdown", "id": "fdeabc4a", "metadata": {}, "source": [ "## 0. Setup and data" ] }, { "cell_type": "code", "execution_count": 1, "id": "806082e0", "metadata": { "execution": { "iopub.execute_input": "2026-07-20T04:18:18.176205Z", "iopub.status.busy": "2026-07-20T04:18:18.176031Z", "iopub.status.idle": "2026-07-20T04:18:21.342354Z", "shell.execute_reply": "2026-07-20T04:18:21.341655Z" } }, "outputs": [], "source": [ "import time, warnings\n", "warnings.filterwarnings(\"ignore\")\n", "import numpy as np\n", "import torch\n", "import matplotlib.pyplot as plt\n", "\n", "torch.set_default_dtype(torch.float64)\n", "torch.manual_seed(0)\n", "\n", "from xnn.common.data import load_dataset" ] }, { "cell_type": "code", "execution_count": 2, "id": "8c1f54bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train 1000 test 500 atoms/molecule 9\n" ] } ], "source": [ "# The paper trains without a distance cutoff -- its RBF grid just ends beyond\n", "# every pair distance. Ethanol spans < 5 A, so a 10 A radius reproduces that\n", "# no-cutoff setting (complete graph) with a matching 0.1 A RBF grid\n", "CUTOFF, N_RBF = 10.0, 101\n", "\n", "# Load the RMD17 ethanol dataset, which is small enough to fit in memory\n", "splits = load_dataset(\"rmd17\", molecule=\"ethanol\", n_train=1000, n_test=500, quiet=True)\n", "\n", "# Split the dataset into training and testing sets\n", "train_structs, test_structs = splits[\"train\"], splits[\"test\"]\n", "\n", "# Count the number of atoms in the first training structure\n", "N_ATOMS = len(train_structs[0][\"atomic_numbers\"])\n", "\n", "# Print the number of training and testing structures, as well as the number of\n", "# atoms per molecule\n", "print(\n", " f\"train {len(train_structs)} test {len(test_structs)} \"\n", " f\"atoms/molecule {N_ATOMS}\"\n", ")" ] }, { "cell_type": "markdown", "id": "c4363c0a", "metadata": {}, "source": [ "## 1. Per-atom energy standardization\n", "\n", "SchNet inherits the Deep Tensor Neural Netrowk (DTNN)'s output convention: the\n", "network predicts a *standardized* per-atom energy $\\hat E_i$, and the final\n", "contribution is\n", "\n", "$$E_i = E_\\sigma \\hat E_i + E_\\mu$$\n", "\n", "where $E_\\mu$ and $E_\\sigma$ denote the mean and standard deviation of the\n", "**energy per atom** over the training set, respectively. With the\n", "zero-initialized output head, the model's first prediction is exactly the\n", "training-set mean which may provide a good starting point for the training." ] }, { "cell_type": "code", "execution_count": 3, "id": "bf794d09", "metadata": { "execution": { "iopub.execute_input": "2026-07-20T04:18:21.344112Z", "iopub.status.busy": "2026-07-20T04:18:21.343843Z", "iopub.status.idle": "2026-07-20T04:18:21.348367Z", "shell.execute_reply": "2026-07-20T04:18:21.347562Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "E_mu = -467.7363 eV/atom E_sigma = 0.01981 eV/atom\n", "(total-energy spread of the trajectory: 0.178 eV)\n" ] } ], "source": [ "# Compute the mean and standard deviation of the energy per atom in the training\n", "# set\n", "E_train = np.array([s[\"energy\"] for s in train_structs])\n", "E_SHIFT = float(E_train.mean() / N_ATOMS) # E_mu (eV / atom)\n", "E_SCALE = float(E_train.std() / N_ATOMS) # E_sigma (eV / atom)\n", "\n", "# Print the mean and standard deviation of the energy per atom, as well as the\n", "# total-energy spread of the trajectory\n", "print(f\"E_mu = {E_SHIFT:.4f} eV/atom E_sigma = {E_SCALE:.5f} eV/atom\")\n", "print(f\"(total-energy spread of the trajectory: {E_train.std():.3f} eV)\")" ] }, { "cell_type": "markdown", "id": "17d1bec6", "metadata": {}, "source": [ "## 2. Build SchNet and train on energies + forces\n", "\n", "The [SchNet manuscript](https://arxiv.org/pdf/1706.08566)'s combined loss is\n", "\n", "$$\\ell = \\rho\\,\\|E - \\hat E\\|^2 + \\frac{1}{n}\\sum_i \\|F_i + \\partial\\hat\n", "E/\\partial R_i\\|^2$$\n", "\n", "with $\\rho = 0.01$. The `xnn` trainer normalizes the energy term per atom so\n", "`energy_weight=1, force_weight=1` gives an effective energy/force ratio of\n", "$1/n^2 = 1/81 \\approx 0.012$, the paper's $\\rho$. The `trainer` section of the\n", "config picks the `best.pt` and the plateau scheduler follows the validation loss\n", "and the error metrics that are being computed. The loggers decide where the\n", "metrics will be stored/printed: a `CSVLogger` keeps every epoch in\n", "`runs/schnet_rmd17/metrics.csv`, and a `ConsoleLogger` subclass prints to the\n", "standard output interface every 50th epoch." ] }, { "cell_type": "code", "execution_count": 4, "id": "9ce2c350", "metadata": { "execution": { "iopub.execute_input": "2026-07-20T04:18:21.349739Z", "iopub.status.busy": "2026-07-20T04:18:21.349612Z", "iopub.status.idle": "2026-07-20T04:22:13.625476Z", "shell.execute_reply": "2026-07-20T04:22:13.624492Z" } }, "outputs": [], "source": [ "from torch.utils.data import Subset\n", "from xnn.common.data import AtomicDataset\n", "from xnn.common.config import (\n", " Config,\n", " ModelConfig,\n", " DataConfig,\n", " OptimConfig,\n", " TrainerConfig,\n", ")\n", "from xnn.common.train import ConsoleLogger, CSVLogger, Trainer" ] }, { "cell_type": "code", "execution_count": 5, "id": "719fc687", "metadata": {}, "outputs": [], "source": [ "# Training hyperparameters\n", "EPOCHS, BS, LR = 300, 32, 1e-3\n", "\n", "# Create the training and testing datasets using the AtomicDataset class, with\n", "# the specified cutoff distance\n", "train_ds = AtomicDataset(train_structs, CUTOFF)\n", "test_ds = AtomicDataset(test_structs, CUTOFF)\n", "\n", "# Create a validation set of 100 structures from the training set, and use the\n", "# remaining structures for training\n", "n_val = 100\n", "val_idx = list(range(n_val))\n", "tr_idx = list(range(n_val, len(train_ds)))\n", "\n", "# Create a configuration object for the SchNet model, specifying the model\n", "# architecture, data parameters, optimization settings, and output directory\n", "cfg = Config(\n", " model=ModelConfig(\n", " name=\"schnet\",\n", " cutoff=CUTOFF,\n", " n_features=64,\n", " n_interactions=3,\n", " n_rbf=N_RBF,\n", " extra={\"gamma\": 10.0, \"energy_shift\": E_SHIFT, \"energy_scale\": E_SCALE},\n", " ),\n", " data=DataConfig(cutoff=CUTOFF, batch_size=BS),\n", " optim=OptimConfig(\n", " lr=LR, epochs=EPOCHS, energy_weight=1.0, force_weight=1.0, scheduler=\"plateau\"\n", " ),\n", " trainer=TrainerConfig(monitor=\"val/loss\", metrics=[\"mae\", \"rmse\"]),\n", " output_dir=\"runs/schnet_rmd17\",\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "id": "2b799889", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SchNet parameters: 78,117 | device: cuda\n", "epoch 49 | train loss 2.4335e-02 (energy 2.068e-04, force 2.413e-02) | val loss 3.5942e-02 (energy 1.151e-04, force 3.583e-02)\n", " val mae: energy 9.378e-03, forces 1.339e-01 | rmse: energy 1.073e-02, forces 1.893e-01\n", "epoch 99 | train loss 1.3431e-02 (energy 1.310e-04, force 1.330e-02) | val loss 1.6855e-02 (energy 1.426e-04, force 1.671e-02)\n", " val mae: energy 1.090e-02, forces 9.006e-02 | rmse: energy 1.194e-02, forces 1.293e-01\n", "epoch 149 | train loss 9.3349e-03 (energy 6.929e-05, force 9.266e-03) | val loss 1.1998e-02 (energy 1.633e-05, force 1.198e-02)\n", " val mae: energy 3.217e-03, forces 7.771e-02 | rmse: energy 4.041e-03, forces 1.095e-01\n", "epoch 199 | train loss 5.4274e-03 (energy 2.104e-05, force 5.406e-03) | val loss 7.8981e-03 (energy 3.643e-05, force 7.862e-03)\n", " val mae: energy 5.387e-03, forces 6.109e-02 | rmse: energy 6.036e-03, forces 8.867e-02\n", "epoch 249 | train loss 5.0365e-03 (energy 9.388e-06, force 5.027e-03) | val loss 7.3526e-03 (energy 9.181e-06, force 7.343e-03)\n", " val mae: energy 2.431e-03, forces 5.914e-02 | rmse: energy 3.030e-03, forces 8.569e-02\n", "epoch 299 | train loss 4.9968e-03 (energy 9.328e-06, force 4.987e-03) | val loss 7.3143e-03 (energy 9.284e-06, force 7.305e-03)\n", " val mae: energy 2.480e-03, forces 5.891e-02 | rmse: energy 3.047e-03, forces 8.547e-02\n", "test loss 6.9461e-03 (energy 1.131e-05, force 6.935e-03)\n", " test mae: energy 2.591e-03, forces 5.914e-02 | rmse: energy 3.364e-03, forces 8.328e-02\n", "trained 300 epochs in 649s\n" ] } ], "source": [ "class EveryNEpochs(ConsoleLogger):\n", " \"\"\"Prints the epoch lines of every n-th epoch and the test result.\"\"\"\n", "\n", " def __init__(self, n):\n", " super().__init__()\n", " self.n = n\n", "\n", " def log_metrics(self, metrics, step):\n", " if \"epoch\" not in metrics or (metrics[\"epoch\"] + 1) % self.n == 0:\n", " super().log_metrics(metrics, step)\n", "\n", "\n", "# Every epoch goes to runs/schnet_rmd17/metrics.csv, every 50th to the screen\n", "csv_logger = CSVLogger()\n", "trainer = Trainer(\n", " cfg,\n", " Subset(train_ds, tr_idx),\n", " Subset(train_ds, val_idx),\n", " test_ds,\n", " logger=[csv_logger, EveryNEpochs(50)],\n", ")\n", "\n", "# Count the number of parameters in the SchNet model and print it along with the\n", "# device being used for training\n", "n_params = sum(p.numel() for p in trainer.module.parameters())\n", "print(f\"SchNet parameters: {n_params:,} | device: {trainer.device}\")\n", "\n", "# Train the SchNet model for the specified number of epochs and print the total\n", "# training time\n", "t0 = time.time()\n", "metrics = trainer.fit()\n", "print(f\"trained {EPOCHS} epochs in {time.time()-t0:.0f}s\")" ] }, { "cell_type": "markdown", "id": "5c344e22", "metadata": {}, "source": [ "## 3. Learning curves\n", "\n", "The loss and the validation MAEs of every epoch, read back from `metrics.csv`\n", "(the energy MAE is per atom), can be plotted as shown below" ] }, { "cell_type": "code", "execution_count": 7, "id": "064da43f", "metadata": { "execution": { "iopub.execute_input": "2026-07-20T04:22:13.627513Z", "iopub.status.busy": "2026-07-20T04:22:13.627194Z", "iopub.status.idle": "2026-07-20T04:22:14.611405Z", "shell.execute_reply": "2026-07-20T04:22:14.610342Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import csv\n", "\n", "# One row per epoch, then one row with the test metrics\n", "with open(csv_logger.path) as f:\n", " rows = [r for r in csv.DictReader(f) if r[\"epoch\"]]\n", "\n", "# Extract the data for plotting\n", "col = lambda key: np.array([float(r[key]) for r in rows])\n", "epoch = col(\"epoch\") + 1\n", "\n", "# Plot the training and validation loss\n", "fig, (a1, a2) = plt.subplots(1, 2, figsize=(9, 3.2))\n", "a1.plot(epoch, col(\"train/loss\"), label=\"train\")\n", "a1.plot(epoch, col(\"val/loss\"), label=\"val\")\n", "a1.set_yscale(\"log\")\n", "a1.set_xlabel(\"epoch\")\n", "a1.set_ylabel(\"loss\")\n", "a1.legend()\n", "a2.plot(epoch, col(\"val/energy_mae\") * 1e3, label=\"energy (meV/atom)\")\n", "a2.plot(epoch, col(\"val/forces_mae\") * 1e3, label=\"forces (meV/A)\")\n", "a2.set_yscale(\"log\")\n", "a2.set_xlabel(\"epoch\")\n", "a2.set_ylabel(\"validation MAE\")\n", "a2.legend()\n", "fig.suptitle(\"SchNet on rMD17 ethanol (energies + forces)\")\n", "fig.tight_layout()\n", "fig.savefig(\"schnet_loss_curves.png\", dpi=110)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ce415b24", "metadata": {}, "source": [ "## 4. Energy & force parity vs DFT\n", "\n", "We load `best.pt`, the weights of the epoch with the lowest validation loss (the\n", "checkpoint `schnet_ethanol_md.ipynb` runs), predict on the 500 held-out\n", "conformations and compare to the reference DFT values. The MAEs are in kcal/mol\n", "with the energy error per molecule in order to be consist with the [SchNet\n", "manuscript](https://arxiv.org/pdf/1706.08566). \n", "\n", "For comparison: SchNet, trained on $N = 1000$ MD17 ethanol conformations,\n", "reaches MAE of 0.08 kcal/mol (energy) and 0.39 kcal/mol/Å (forces) in Table 2 of\n", "the [SchNet manuscript](https://arxiv.org/pdf/1706.08566), with $\\approx$ 10x\n", "longer training and learning-rate decay. This 300-epoch run lands within a few\n", "times those numbers (sub-kcal/mol energies)." ] }, { "cell_type": "code", "execution_count": 8, "id": "7cdd2d72", "metadata": { "execution": { "iopub.execute_input": "2026-07-20T04:22:14.615430Z", "iopub.status.busy": "2026-07-20T04:22:14.615292Z", "iopub.status.idle": "2026-07-20T04:22:15.461723Z", "shell.execute_reply": "2026-07-20T04:22:15.450525Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "energy MAE : 23.62 meV (0.545 kcal/mol)\n", "force MAE : 59.15 meV/A (1.364 kcal/mol/A)\n", "energy RMSE: 30.68 meV (0.707 kcal/mol)\n", "force RMSE: 83.28 meV/A (1.921 kcal/mol/A)\n" ] } ], "source": [ "from torch.utils.data import DataLoader\n", "from xnn.common.data import collate\n", "from xnn.common.models.hub import from_pretrained\n", "\n", "# Set the device to the one used by the trainer\n", "device = str(trainer.device)\n", "\n", "# Load the best model checkpoint from the training run and set it to evaluation\n", "# mode\n", "model = from_pretrained(\"runs/schnet_rmd17/best.pt\", device=device).eval()\n", "\n", "# Define the conversion factor from electron volts to kilocalories per mole\n", "EV2KCAL = 23.060541945329334\n", "\n", "# Evaluate the model on the test dataset and compute the predicted and reference\n", "# energies and forces\n", "E_pred, E_ref, F_pred, F_ref = [], [], [], []\n", "for batch in DataLoader(test_ds, batch_size=50, collate_fn=collate):\n", " batch = batch.to(device)\n", " out = model(batch)\n", " E_pred.append(out[\"energy\"].detach().cpu().numpy())\n", " E_ref.append(batch.energy.detach().cpu().numpy())\n", " F_pred.append(out[\"forces\"].detach().cpu().numpy())\n", " F_ref.append(batch.forces.detach().cpu().numpy())\n", "E_pred, E_ref = np.concatenate(E_pred), np.concatenate(E_ref)\n", "F_pred, F_ref = np.concatenate(F_pred), np.concatenate(F_ref)\n", "\n", "# Compute the mean absolute error (MAE) and root mean square error (RMSE) for\n", "# both energies and forces, and print the results in both meV and kcal/mol units\n", "e_mae = np.abs(E_pred - E_ref).mean()\n", "f_mae = np.abs(F_pred - F_ref).mean()\n", "e_rmse = np.sqrt(np.mean((E_pred - E_ref) ** 2))\n", "f_rmse = np.sqrt(np.mean((F_pred - F_ref) ** 2))\n", "print(f\"energy MAE : {e_mae*1e3:6.2f} meV ({e_mae*EV2KCAL:.3f} kcal/mol)\")\n", "print(f\"force MAE : {f_mae*1e3:6.2f} meV/A ({f_mae*EV2KCAL:.3f} kcal/mol/A)\")\n", "print(f\"energy RMSE: {e_rmse*1e3:6.2f} meV ({e_rmse*EV2KCAL:.3f} kcal/mol)\")\n", "print(f\"force RMSE: {f_rmse*1e3:6.2f} meV/A ({f_rmse*EV2KCAL:.3f} kcal/mol/A)\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "6bbf5993", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the parity between the predicted and reference energies and forces, with\n", "# the mean absolute error (MAE) displayed in the titles \n", "fig, (a1, a2) = plt.subplots(1, 2, figsize=(9, 4))\n", "a1.scatter(E_ref - E_ref.mean(), E_pred - E_ref.mean(), s=8, alpha=0.5)\n", "lim = np.array([(E_ref - E_ref.mean()).min(), (E_ref - E_ref.mean()).max()])\n", "a1.plot(lim, lim, \"k--\", lw=1)\n", "a1.set_xlabel(\"DFT energy (eV, centered)\")\n", "a1.set_ylabel(\"SchNet energy (eV, centered)\")\n", "a1.set_title(f\"energy MAE {e_mae*EV2KCAL:.3f} kcal/mol\")\n", "a2.scatter(F_ref.ravel(), F_pred.ravel(), s=3, alpha=0.2)\n", "lim = np.array([F_ref.min(), F_ref.max()])\n", "a2.plot(lim, lim, \"k--\", lw=1)\n", "a2.set_xlabel(\"DFT force (eV/A)\"); a2.set_ylabel(\"SchNet force (eV/A)\")\n", "a2.set_title(f\"forces MAE {f_mae*EV2KCAL:.3f} kcal/mol/A\")\n", "fig.tight_layout()\n", "fig.savefig(\"schnet_parity.png\", dpi=110)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "45441870", "metadata": {}, "source": [ "## 5. A smooth 1-D potential-energy scan\n", "\n", "One of the reasons behind creating a *continuous* filter in SchNet is having a\n", "smooth, differentiable potential energy surface (the paper's Fig. 1 contrasts\n", "this with the jagged surface a discretized filter produces). We stretch the C-O\n", "bond of a test geometry and evaluate the trained SchNet along the scan through\n", "its ASE calculator: a smooth single-well curve is what enables stable geometry\n", "optimization and MD." ] }, { "cell_type": "code", "execution_count": 10, "id": "8bded005", "metadata": { "execution": { "iopub.execute_input": "2026-07-20T04:22:15.464063Z", "iopub.status.busy": "2026-07-20T04:22:15.463395Z", "iopub.status.idle": "2026-07-20T04:22:16.654883Z", "shell.execute_reply": "2026-07-20T04:22:16.648241Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ase import Atoms\n", "from xnn.common.deploy import XNNCalculator\n", "\n", "# Create an ASE calculator using the trained SchNet model, with the specified\n", "# cutoff distance and device\n", "calc = XNNCalculator(model, cutoff=CUTOFF, device=device)\n", "\n", "# Extract the atomic numbers and positions of the first test structure, and\n", "# create an ASE Atoms object with the SchNet calculator\n", "s = test_structs[0]\n", "atoms = Atoms(numbers=s[\"atomic_numbers\"], positions=s[\"pos\"], calculator=calc)\n", "Z, pos0 = atoms.numbers, atoms.positions.copy()\n", "\n", "# Ethanol: stretch the C-O bond (the O and the nearest C)\n", "O = int(np.flatnonzero(Z == 8)[0])\n", "Cs = np.flatnonzero(Z == 6)\n", "C = int(Cs[np.linalg.norm(pos0[Cs] - pos0[O], axis=1).argmin()])\n", "axis = pos0[O] - pos0[C]\n", "r0 = float(np.linalg.norm(axis))\n", "axis /= r0\n", "\n", "# Perform a bond scan by varying the C-O distance and computing the potential\n", "# energy at each distance using the SchNet calculator. The energies are then\n", "# shifted so that the minimum energy is zero\n", "scan = np.linspace(-0.35, 0.6, 60)\n", "energies = []\n", "for dr in scan:\n", " pos = pos0.copy(); pos[O] += dr * axis\n", " atoms.positions = pos\n", " energies.append(atoms.get_potential_energy())\n", "energies = np.array(energies) - min(energies)\n", "\n", "# Plot the potential energy surface (PES) of the C-O bond scan, with the C-O\n", "# distance on the x-axis and the relative energy on the y-axis\n", "fig, ax = plt.subplots(figsize=(5, 3.2))\n", "ax.plot(r0 + scan, energies, \"o-\", ms=3)\n", "ax.set_xlabel(\"C-O distance (A)\"); ax.set_ylabel(\"relative energy (eV)\")\n", "ax.set_title(\"SchNet C-O bond scan (smooth PES)\")\n", "fig.tight_layout()\n", "fig.savefig(\"schnet_bond_scan.png\", dpi=110)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e4910380", "metadata": {}, "source": [ "## Summary\n", "\n", "- The `xnn` SchNet reproduces the [SchNet\n", " manuscript](https://arxiv.org/pdf/1706.08566)'s results on the rMD17 ethanol\n", " dataset, achieving sub-kcal/mol accuracy in energy and force predictions.\n", "- The model's continuous filter architecture allows for smooth potential energy\n", " surfaces, which is crucial for stable geometry optimization and molecular\n", " dynamics simulations.\n", "- The training process, including energy and force loss weighting, learning rate\n", " scheduling, and logging, is effectively managed by the `xnn` Trainer,\n", " demonstrating the framework's capability for handling complex machine learning\n", " tasks in computational chemistry." ] }, { "cell_type": "markdown", "id": "19fd64c5", "metadata": {}, "source": [ "## References\n", "\n", "- [Schütt et al., *SchNet: A continuous-filter convolutional neural network for modeling quantum interactions* arXiv.1706.08566 (2017)](https://arxiv.org/pdf/1706.08566)\n", "- [Schütt et al., *SchNet: A deep learning architecture for molecules and materials*, J. Chem. Phys. 148, 241722 (2018)](https://doi.org/10.1063/1.5019779)" ] } ], "metadata": { "kernelspec": { "display_name": "xnns (3.13.12)", "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": "3.13.12" } }, "nbformat": 4, "nbformat_minor": 5 }