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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 Multi-Surrogate Strategies for Multi-Objective Bayesian Optimization: A Cross-Phase Empirical Study from Static Baselines to LLM-Proposed Policies
Author(s) Ahnaf Alvi, Shakti Prasad Padhy, Jan Janssen, Danny Perez, Douglas Allaire, Raymundo Arroyave
On-Site Speaker (Planned) Ahnaf Alvi
Abstract Scope The surrogate model is the central yet least-validated lever in multi-objective Bayesian optimization (MO-BO), the dominant framework for small-budget materials discovery. We present the largest cross-phase evaluation to date: thirteen surrogates(Gaussian processes, quantile and tree-based regressors, a prior-fitted transformer) paired with three acquisition functions across twenty-two problems—eighteen synthetic and four newly released materials benchmarks(multi-principal element alloys, titanium alloys, perovskite photovoltaics, and joined Matbench bulk-and-shear modulus)—under four selection policies(static, manual switching, adaptive switching, and one-shot large-language-model-emitted policies), totaling 26,721 runs. Three findings emerge. On the materials benchmarks, the multitask Gaussian process attains the highest hypervolume on three of four problems. Nine of ten independent LLM agents converge on a near-identical policy, yet their per-benchmark outcomes remain uncorrelated(r = −0.10). Finally, the per-iteration Spearman correlation between predicted and observed objectives predicts final hypervolume at zero cost, providing an actionable surrogate-trustworthiness signal for uncertainty-aware, agent-assisted materials discovery.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering,

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