Molecular simulation-derived features for machine learning predictions of metal glass forming ability

TL;DR

This paper introduces a machine learning approach using molecular dynamics-derived features to predict metal glass forming ability, emphasizing computational efficiency and improved accuracy over previous models.

Contribution

It presents novel, scalable features from molecular dynamics simulations that enhance GFA prediction models and identifies key features like enthalpy of crystallization and icosahedral fraction.

Findings

Molecular dynamics features improve GFA prediction accuracy.

Two key features significantly enhance model performance.

Features are computationally accessible and scalable.

Abstract

We have developed models of metallic alloy glass forming ability based on newly computationally accessible features obtained from molecular dynamics simulations. In this work we showed that it is possible to increase the predictive value of GFA models by using input features obtained from molecular dynamics simulations. Such features require only relatively straightforward and scalable simulations, making them significantly easier and less expensive to obtain than experimental measurements. We generated a database of molecular dynamics critical cooling rates along with associated candidate features that are inspired from previous research on GFA. Out of the list of 9 proposed GFA features, we identify two as being the most important to performance through a LASSO model. Enthalpy of crystallization and icosahedral-like fraction at 100 K showed promise because they enable a significant improvement to model performance and because they are accessible to flexible ab initio quantum mechanical methods readily applicable to almost all systems. This advancement in computationally accessible features for machine learning predictions GFA will enable future models to more accurately predict new glass forming alloys.