Fast Generation of Ab Initio Training Data for Large-Scale Applications of Neural Network Potentials

Fast, accurate calculation of phonon dispersion in large crystal systems proves to be an ongoing challenge due to cubic scaling of total energy calculation using traditional ab initio methods and lack of sufficient accuracy using empirical force field methods. Neural network potentials (NNPs) have recently shown great promise in speeding up the computation, but generating a high-quality training dataset for NNPs has involved taking snapshots from ab initio molecular dynamics simulations, which can take large computational resources by itself, bringing viability of developing and using NNPs for studying phonon properties into question. We propose a method to quickly generate a dataset to train a NNP tailored to perform well on the target system of interest. Taking a regular AB-stacked bilayer graphene unit cell containing 4 atoms, we systematically perform translation, supersizing, and random displacement of atomic positions to generate O(10⁴) small structures, whose total energies can be calculated in an embarrassingly parallel fashion. Using this dataset, we show a good performance in computing total energies and interatomic forces for twisted bilayer graphene structures compared to previous NNP models.