Self-learning Hybrid Monte Carlo method for first-principles molecular simulations

We propose a novel approach called Self-Learning Hybrid Monte Carlo (SLHMC)[1] which is a general method to make use of machine learning potentials to accelerate the statistical sampling of first-principles density-functional-theory (DFT) simulations. The trajectories are generated on an approximate machine learning (ML) potential energy surface. The trajectories are then accepted or rejected by the Metropolis algorithm based on DFT energies. In this way the statistical ensemble is sampled exactly at the DFT level for a given thermodynamic condition. Meanwhile the ML potential is improved on the fly by training to enhance the sampling, whereby the training data set, which is sampled from the exact ensemble, is created automatically.
[1]Yuki Nagai, Masahiko Okumura, Keita Kobayashi, and Motoyuki Shiga, arXiv:1909.02255