Mechanical properties of single and polycrystalline solids from machine learning

TL;DR

This paper introduces a machine learning-based method for efficiently calculating the elastic and mechanical properties of complex polycrystalline and multi-phase solids with high accuracy, overcoming computational challenges of traditional methods.

Contribution

The authors develop a machine learning interatomic potential trained on local fragments to enable large-scale, accurate mechanical property calculations of complex solids.

Findings

Elastic moduli of polycrystalline diamond depend on grain size

Method achieves high accuracy comparable to ab initio calculations

Enables large-scale simulations of realistic solid structures

Abstract

Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of $ab$ $initio$ methods and low accuracy of empirical potentials. We propose a computational technique towards efficient calculations of mechanical properties of polycrystals, composites, and multi-phase systems from atomistic simulation with high accuracy and reasonable computational cost. It is based on using actively learned machine learning interatomic potentials (MLIPs) trained on a local fragments of the polycrystalline system for which forces, stresses and energies are computed by using $ab$ $initio$ calculations. Developed approach is used for calculation the dependence of elastic moduli of polycrystalline diamond on the grain size. This technique allows one to perform large-scale calculations of mechanical properties of complex solids of various compositions and structures with high accuracy making the transition from ideal (single crystal) systems to more realistic ones.