Investigating the influence of local composition on properties in complex alloys using machine learned interatomic potentials

Complex concentrated alloys (CCAs) contain high concentrations of three or more metallic elements which mix exceptionally well, down to the atomic scale. In contrast to conventional alloy properties, CCA properties are highly sensitive to local chemistry and the specific scale of property measurement. For a multitude of reasons, molecular dynamics (MD) stands out as uniquely suited to probe this distinctive scale-sensitivity. However, there are two major, interrelated challenges to tackle before MD can make real inroads into this area. The first is the development of transferrable interatomic potentials that can accurately model CCAs through widely varying chemical environments. Highly accurate machine-learned interatomic potentials (MLIAPs) hold enormous promise as a solution, but training and validating CCA MLIAPs is still a nascent field. The second challenge is that, even with excellent CCA MLIAPs, little progress can be made without ways to define, track, and compare the ‘local’ alloy compositions contained in a CCA sample. Relying exclusively on a traditional ‘global’ alloy composition label (such as ‘equiatomic’) to describe properties hinders understanding of the varied nanoscale chemical interactions that characterize CCAs.

In this presentation, we will discuss our group’s progress in addressing both challenges. We will show successful approaches to developing robust, multi-compositional CCA MLIAPs using the spectral neighbor analysis potential (SNAP) formalism. Using SNAP models of the MoNbTaTi(W) refractory CCA family, we will demonstrate how metrics built in an alloy’s composition space can be used to connect local and global CCA properties in already-trained MLIAPs and be used to make data-driven decisions in the training of new ones. Finally, we will discuss the potential these advances have to influence alloy characterization, design, and property prediction. Progress in defining training robustness in this space translates broadly to many supervised machine learning tasks.