Training Machine Learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs

Though machine-learning interatomic potentials (MLIAPs) have greatly improved the accuracy of molecular dynamics (MD) simulations in recent years, there is still much to be learned in the training for chemically complex materials. One important example can be found in complex concentrated alloys (CCAs), which contain high concentrations of three or more metallic elements. While excellent progress has been made in generating CCA MLIAPs for a single (usually equiatomic) composition, far less is understood about how to create generalized and accuracy transferrable potentials over a broad range of compositions. This capability is a critical component of large-scale modeling of CCAs, as their chemical complexity can result in large variability in local properties. Additionally, transferability is critical when using simulation predictions for alloy design. In this talk, we will discuss the methodology behind the development of a generalized refractory CCA MLIAP using the spectral neighbor analysis potential (SNAP) method and demonstrate how it can be used to model CCA properties and behavior in extremes of temperature and strain rate.