Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling (2307.13710v1)

Abstract: Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important roles in the computational understanding and design of materials. However, MLIPs are only as accurate and robust as the data they are trained on. In this work, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolate more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with universal potentials such as M3GNet can be used in place of expensive \textit{ab initio} MD to rapidly create a large configuration space for target materials systems. Combined with DIRECT sampling, we develop a highly reliable moment tensor potential for Ti-H system without the need for iterative optimization. This work paves the way towards robust high throughput development of MLIPs across any compositional complexity.