Composition design of refractory compositionally complex alloys using machine learning models

Abstract

Refractory compositionally complex alloys (RCCAs) are considered the next generation high-temperature materials. However, their high-dimensional composition spaces are too large to explore by traditional density functional theory or experimental means, making new RCCA discovery slow and cumbersome. This work has addressed these challenges with an integrated composition design framework that can efficiently and exhaustively explore the relationship between the compositions and two fundamental aspects: 1) the phase stability, including the target body-centered cubic (BCC) phase and its competing phases (hexagonal closed-pack (HCP) structures, Laves and B2 intermetallic phases), and 2) the mechanical properties. This framework is demonstrated with RCCAs within nine refractory metals (Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, and W). Theory-guided machine learning (ML) models were employed to find the composition-mechanical property relationship of RCCAs, where the established theory is used to supplement the yield strength data at ultra-high temperature, and a forward sequential feature selection (SFS) is used to determine feature selection. The resulting ML model for temperature-dependent yield strength was found to have an R_squared value of 0.98 over the entire temperature range (from 0 to 2000 K). The impact of each constituent element on the six key properties is evaluated. The addition of Nb tends to stabilize the BCC phase and the addition of Ti improves the ductility of RCCAs. Combined with all methods involved in this framework, the on-demand designer allows the alloy designers to have all properties for any RCCA compositions and narrow down the composition space by applying custom screening criteria. The output from the predictor and screener provides valuable guidance for our experimental study of RCCAs and accelerates the pace of materials discovery.