This is dataset for the publication "Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles", by A.R. Tan, S. Urata, S. Goldman, J. C. B. Dietschreit, and R. Gomez-Bombarelli. The repository contains the simulation and adversarially sampled data to train and test the neural network potentials. The contents of the datasets are:
ammonia_train.xyz: contains all geometries, energies and forces for the ammonia molecule, as calculated with DFT. The content in this file is downloaded from the paper Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks.ammonia_test.xyz: contains geometries, energies and forces for the ammonia molecule, sampled using the adversarial sampling method and calculated with DFT. Geometries in this file is used to test robustness and extrapolative power of the neural network potentials.silica_train.xyz: contains all structures, lattice cells, energies and forces for the silica glass system. Structures here are sampled from simulations using force-matching potentials (FMP) from paper How fluorine minimizes density fluctuations of silica glass: Molecular dynamics study with machine-learning assisted force-matching potential, and calculated using DFT. Note that DFT energies in this file has been subtracted by a value of -125667.96875 kcal/mol.silica_test.xyz: contains all structures, lattice cells, energies and forces for the silica glass system, sampled using the adversarial sampling method and calculated with DFT. Structuresin this file is used to test robustness and extrapolative power of the neural network potentials. Note that DFT energies in this file has been subtracted by a value of -125667.96875 kcal/mol.In all datasets, the units for energy is given in kcal/mol, and the units for the forces are given in kcal/mol/Angstrom. Positions and lattice parameters are given in Angstrom.