Chemical Bonding in Metallic Glasses from Machine Learning and Crystal Orbital Hamilton Population

Abstract

The chemistry (composition and bonding information) of metallic glasses (MGs) is at least as important as structural topology for understanding their properties and production/processing peculiarities. This article reports a machine learning (ML)-based approach that brings an unprecedented "big picture" view of chemical bond strengths in MGs of a prototypical alloy system. The connection between electronic structure and chemical bonding is given by crystal orbital Hamilton population (COHP) analysis within the framework of density functional theory (DFT). The stated comprehensive overview is made possible through a combination of efficient quantitative estimate of bond strengths supplied by COHP analysis, representative statistics regarding structure in terms of atomic configurations achieved with classical molecular dynamics simulations, and the smooth overlap of atomic positions (SOAP) descriptor. The study is supplemented by an application of that ML model under the scope of mechanical loading, in which predicted bond strengths enabled atom categorization based on a descriptor of the short-range order possessing a superior chemical sense; a key component to uncover structural/chemical heterogeneity and its influence on mechanical relaxation processes and atomic scale flow mechanisms in MGs.