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

BT Afflerbach and L Schultz and JH Perepezko and PM Voyles and I Szlufarska and D Morgan, COMPUTATIONAL MATERIALS SCIENCE, 199, 110728 (2021).

DOI: 10.1016/j.commatsci.2021.110728

We have developed models of metallic alloy glass forming ability based on newly computationally accessible features obtained from molecular dynamics simulations. Since the discovery of metallic glasses, there have been efforts to predict glass forming ability (GFA) for new alloys. Effective evaluations of GFA have been obtained but generally relied on knowledge of alloy characteristic temperatures like the glass transition, crystallization, and liquidus temperatures but are of limited utility because these features require synthesizing and characterizing the alloy of interest. More recently, machine learning approaches to predict GFA have employed more accessible model features such as the elemental properties of constituent elements. However, these more accessible features generally provide less predictive accuracy than their less accessible counterparts. 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.

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