Accessing negative Poisson`s ratio of graphene by machine learning interatomic potentials

TL;DR

This paper uses machine learning to develop interatomic potentials for graphene, revealing that bond angle increase, not bond length, causes negative Poisson's ratio, aligning with first-principles results and improving simulation accuracy.

Contribution

The study constructs a machine learning-based interatomic potential for graphene, clarifying the mechanism behind its negative Poisson's ratio and enhancing MD simulation fidelity.

Findings

Bond angle increase causes NPR in graphene.

ML interatomic potentials align MD results with first-principles.

Improves accuracy of molecular dynamics simulations.

Abstract

The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential (MTP) for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials. *Author