A Multiple-Fidelity Method for Accurate Simulation of MoS2 Properties Using JAX-ReaxFF and Neural Network Potentials.

Abstract

Reactive force field (ReaxFF) is a commonly used force field for modeling chemical reactions at the atomic level. Recently, JAX-ReaxFF, combined with automatic differentiation, has been used to efficiently parametrize ReaxFF. However, its analytical formula may lead to inaccurate predictions. While neural network-based potentials (NNPs) trained on density functional theory-labeled data offer a more accurate method, it requires a large amount of training data to be trained from scratch. To overcome these issues, we present a multiple-fidelity method that combines JAX-ReaxFF and NNP and apply the method on MoS2, a promising two-dimensional semiconductor for flexible electronics. By incorporating implicit prior physical information, ReaxFF can serve as a cost-effective way to generate pretraining data, facilitating more accurate simulations of MoS2. Moreover, in the Mo-S-H system, the pretraining strategy can reduce root-mean-square errors of energy by 20%. This approach can be extended to a wide variety of material systems, accelerating their computational research.