{ "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": [ "