Active learning for oxidation resistance in high temperature structural alloys

Computational tools for materials discovery and design have in the past decade been extensively developed to study a wide range of properties. This talk will focus our recent results on the integration between thermodynamic calculations, first-principles modeling, machine learning, and experimental validation of mechanical properties and oxidation resistance in refractory complex, concentrated alloys (RCCAs). We will present a new machine learning for accelerated materials discovery (ML-AMD) framework that utilizes multi-fidelity and multi-cost experiments with physics-based modeling. New semi-high-throughput methods for characterizing hardness and oxidation resistance will be presented, and methods for implementing high-throughput simulations into the ML-AMD framework will be expounded. Promising alloys will be identified, and strategies for improving the oxidation resistance of RCCAs will be discussed.