Abstract
The discovery and development of advanced materials is a challenging process constrained by the high time and monetary costs of synthesis, processing, and characterization. The underlying design spaces can be enormous, often with multiple competing objectives. Bayesian optimization (BO) provides a principled approach for efficiently navigating such spaces, but most workflows rely on fixed exploration-exploitation policies that lack the capacity to adapt to shifting constraints in dynamic campaigns typical of self-driving laboratories. In this work, we develop a multi-objective BO framework for alloy design under resource constraints, benchmarking strategies for adaptive policy tuning at each iteration. Our evaluation covers a septenary refractory high-entropy alloy (RHEA) system focused on maximizing melting temperature and minimizing density, and an Fe-Co-Ni-based soft magnetic alloy system targeting saturation magnetization, coercivity, and hardness. We compare an exploitation-focused strategy, a fixed mixed exploratory/exploitative policy, and two distinct LLM-based adaptive strategies with different approaches to batch allocation and campaign signal interpretation, evaluated across baseline and mid-campaign resource event conditions including budget reductions, timeline cuts, and combined disruptions. Our results show that mixed allocation strategies accumulate substantially more mutual information than the exploitation-focused baseline at a proportionally smaller cost to hypervolume and optimization speed, with adaptive strategies outperforming a fixed-mixed allocation policy by adjusting their allocation in response to both evolving campaign statistics and resource constraints. These findings suggest that adaptive resource allocation offers a favorable tradeoff for materials discovery campaigns in reducing predictive uncertainty on Pareto-optimal compositions.
Keywords
Publication details
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Cite this article
APA 7
Robinson, R., Padhy, S. P., Sinha, S., Alvi, S. M. A. A., Coronel, J. F., Vela, B., Hastings, T., Allaire, D., & Arróyave, R. (2026). Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery. https://omanscience.com/en/articles/agentic-resource-allocation-for-batch-multi-objective-bayesian-optimization-in-autonomous-materials-discovery
MLA 9
Robinson, Robert, et al. "Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery." https://omanscience.com/en/articles/agentic-resource-allocation-for-batch-multi-objective-bayesian-optimization-in-autonomous-materials-discovery.
Chicago (author–date)
Robinson, Robert, Shakti Prasad Padhy, Sushant Sinha, Sk Md Ahnaf Akif Alvi, Juan Florez Coronel, Brent Vela, Trevor Hastings, Douglas Allaire, and Raymundo Arróyave. 2026. "Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery." https://omanscience.com/en/articles/agentic-resource-allocation-for-batch-multi-objective-bayesian-optimization-in-autonomous-materials-discovery.
Harvard
Robinson, R., Padhy, S. P., Sinha, S., Alvi, S. M. A. A., Coronel, J. F., Vela, B., Hastings, T., Allaire, D. and Arróyave, R. (2026) 'Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery', Available at: https://omanscience.com/en/articles/agentic-resource-allocation-for-batch-multi-objective-bayesian-optimization-in-autonomous-materials-discovery.
Vancouver
Robinson R, Padhy SP, Sinha S, Alvi SMAA, Coronel JF, Vela B, et al. Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery. https://omanscience.com/en/articles/agentic-resource-allocation-for-batch-multi-objective-bayesian-optimization-in-autonomous-materials-discovery
IEEE
R. Robinson, S. P. Padhy, S. Sinha, S. M. A. A. Alvi, J. F. Coronel, B. Vela, T. Hastings, D. Allaire, and R. Arróyave, "Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery," https://omanscience.com/en/articles/agentic-resource-allocation-for-batch-multi-objective-bayesian-optimization-in-autonomous-materials-discovery.