Restricted multicanonical sampling for machine learning potential construction

Abstract

The restricted multicanonical (rMUCA) ensemble method is developed and combined with the on-the-fly machine learning potential (MLP) generation scheme. The rMUCA simulation performs a random walk in the potential-energy subspace restricted by the selected collective variables and allows us to sample physically relevant configurations without being trapped in local energy minima. No preliminary simulation runs are required to construct a bias potential. The sample structures for training are collected dynamically from the simulations using the MLP itself where the simultaneous error estimation is utilized to judge whether an updated structure should be added to the sample data set or not. The rMUCA formula can be also used for the saddle-point search with minor modification. The method is applied to the oxidation of carbon monoxide on platinum surfaces. The results show that the rMUCA simulation provides an efficient and accurate way to sample rare events for the MLP construction.