Dept. of Materials Science and Engineering Spring Seminar Series: Raymundo Arróyave

Description

Raymundo Arróyave, a professor of materials science at Texas A&M University, will give a talk titled "Optimal Discovery of Multi-Dimensional Pareto Sets in High-Entropy Alloy Spaces" for the Department of Materials Science and Engineering.

Abstract:

Recent advancements in materials discovery have shifted from traditional, labor-intensive methods to data-driven, informatics-based approaches. High-throughput (HTP) experimentation and computation dominate current efforts but are constrained by practical limitations such as resource availability and human decision-making bottlenecks. To address these, framing materials discovery as an optimal experimental design problem has emerged as a promising paradigm. In this talk, I will explore the application of Bayesian Optimization in the targeted exploration of the CoCrFeNiVAl FCC high-entropy alloy space. This integrated framework identifies Pareto-optimal solutions across performance metrics, including strength, hardness, and strain rate sensitivity, while leveraging advanced cyber-physical workflows. By querying only 0.15% of this vast design space, our iterative synthesis-characterization-testing loops achieved unprecedented acceleration in discovering optimal alloy compositions. The results highlight the efficiency of Bayesian-guided discovery in navigating complex, multi-objective spaces and the insights gained into the underlying chemical and microstructural drivers of alloy performance. I will also discuss current challenges, the impact of these findings on alloy optimization processes, and future opportunities for integrating artificial-intelligence- / machine-learning-assisted simulations with experimental workflows. This approach represents a significant step toward autonomous materials design and scalable multi-objective materials optimization.