| Abstract Scope |
The rapid discovery of multi-component alloys with optimized properties is challenging, requiring both computational efficiency and precision. Recent Bayesian optimization (BO) advancements in continuous design spaces enhance alloy composition optimization, identifying novel alloys with targeted properties. Multi-objective BO acquisition functions like TSEMO, parEGO, and qNEHVI are benchmarked for efficiency and robustness. To accelerate development, an ICME framework integrates CALPHAD-based simulations for phase stability and thermodynamic predictions, autonomously selecting acquisition functions based on material properties and objectives. Design of Experiments (DOE) strategies minimize validation iterations, reducing timelines, while pool-based active learning enables concurrent computational and experimental work in high-throughput environments. Multi-scale modeling correlates microstructure with performance, with uncertainty quantification ensuring robustness. Data-driven machine learning enables real-time predictions and feedback loops, refining optimization iteratively. This framework, combining advanced optimization, simulations, and experimental validation, provides a scalable solution for rapid discovery and qualification of novel alloys. |