University of North Texas, Department of Materials Science and Engineering

Raymundo Arróyave discusses alloy discovery guided by both metallurgical knowledge and Bayesian optimization. The approach combines physical models, high-throughput CALPHAD calculations, microstructural information and experimental feedback to search chemical and processing spaces that are difficult to explore through data alone. Examples involving high-entropy and refractory alloys address multiple objectives and constraints, including feasibility, correlated properties, uncertainty and microstructural sensitivity. The seminar then connects these decision methods to the Autonomous Robotic Metallurgist under development at his university. This platform links modular synthesis, characterization and testing with digital twins, human–robot collaboration and AI agents. The aim is a closed experimental loop in which scientific judgment and physical understanding guide automated execution.