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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI-Enabled Materials Processing: Integrating Accelerated Experimental Workflows and Processing-Aware Machine Learning
Presentation Title Reexamining Hall–Petch and the Tabor Relation in Concentrated FCC Alloys via BO-Driven Alloy Development
Author(s) Raymundo Arroyave
On-Site Speaker (Planned) Raymundo Arroyave
Abstract Scope Hall–Petch grain-size strengthening and the Tabor hardness–yield relation are canonical empirical descriptions in physical metallurgy, calibrated on conventional alloys with well-characterized processing. Whether they survive in concentrated solid solutions is a question BO-driven alloy development can answer. The BIRDSHOT campaign produced a 93-alloy single-source dataset of FCC HEAs in the Al–Co–Cr–Cu–Fe–Mn–Ni–V system, with paired hardness, yield, and grain-size measurements under controlled processing---one of the largest self-consistent FCC HEA datasets reported. We then compare every candidate scaling law, machine-learning surrogate, and hardness-to-yield conversion against each other and against the data, using criteria that reward both predictive accuracy and parsimony. The results challenge the uniqueness of the classical d^(-1/2) Hall–Petch exponent, identify which hardness-to-yield conversion is defensible for FCC HEAs, and yield a physically interpretable closed-form expression. Understanding the hardness–yield relation has direct value in closed-loop alloy design, where cheap hardness measurements can substitute for expensive tensile testing in early screening.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, ICME, Machine Learning

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