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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery
Author(s) Robert Robinson, Shakti Padhy, Sushant Sinha, Ahnaf Alvi, Juan Florez, Brent Vela, Trevor Hastings, Douglas Allaire, Raymundo Arroyave
On-Site Speaker (Planned) Robert Robinson
Abstract Scope Materials discovery is constrained by costly, time-intensive experimentation across enormous, multi-objective design spaces. Bayesian optimization (BO) offers a principled approach for navigating such spaces, but most workflows rely on fixed exploration-exploitation policies that cannot adapt to shifting constraints in dynamic, self-driving laboratory campaigns. We develop a multi-objective BO framework for alloy design under resource constraints, benchmarking adaptive policy-tuning strategies on a septenary refractory high-entropy alloy system (melting temperature, density) and a Fe-Co-Ni soft-magnetic system (magnetization, coercivity, hardness). We compare an exploitation-focused strategy, a fixed mixed policy, and two LLM-based adaptive strategies in campaigns with and without mid-campaign resource constraints. Mixed allocation strategies accumulate more mutual information than the exploitation-focused baseline at a smaller cost to hypervolume and speed, with adaptive strategies further outperforming the fixed-mixed policy by responding to campaign statistics and resource events. Adaptive resource allocation thus offers a favorable tradeoff for reducing predictive uncertainty on pareto-optimal compositions.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning,

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