Mo-Re-W alloys for high temperature applications: Phase stability, elasticity, and thermal property insights via multi-cell Monte Carlo and machine learning

Abstract

The increasing demand for materials capable of withstanding high temperatures and harsh environments necessitates the discovery of advanced alloys. This study introduces a computational routine to predict solid-state phase stability and calculates elastic constants to determine high temperature viability. With it, machine learning models were trained on 1,014 Mo-Re-W structures to enable a large compilation of elastic and thermal properties over the complete Mo-Re-W compositional domain with extreme resolution. A series of heat maps spanning the full compositional domain were generated to visually present the impact of alloy constituents on the alloy properties. Our findings identified a balanced (Mo,W) + Re blend as a promising composition for high temperature applications, attributed to a strong and stable (Mo,W) matrix with high Re content and the formation of strengthening (W,Re) precipitates that enhanced mechanical performance at 1600 oC. Several Mo-Re-W compositions were manufactured to experimentally validate the computational predictions. This approach provides an efficient and system-agnostic pathway for designing and optimizing alloys for high-temperature applications.