Ab initio framework for deciphering trade-off relationships in multi-component alloys

While first-principles methods have been successfully applied to characterize individual properties of multi-principal element alloys (MPEA), their use to search for optimal trade-offs between competing properties is hampered by high computational demands. In this work, we present a novel framework to explore Pareto-optimal compositions by integrating advanced ab-initio-based techniques into a Bayesian multi-objective optimization method. We benchmark the framework by applying it to solid solution strengthening and ductility of refractory MPEAs, with the parameters of the strengthening and ductility models being efficiently computed using a combination of the coherent-potential approximation method, accounting for finite-temperature effects, and actively-learned moment-tensor potentials parameterized with ab initio data. Additionally, we introduce an analytical model that captures the concentration dependence of all relevant material properties, relying on a few element-specific parameters and universal functions that describe bonding between elements. Our findings offer new crucial insights into the traditional strength-vs-ductility dilemma of refractory MPEAs. The proposed framework is versatile and can be extended to other materials and properties of interest, enabling a predictive and tractable high-throughput screening of Pareto-optimal MPEAs over the entire composition space.