arXiv:2611.12491 · v1
Out-of-the-Box Tabular Foundation Models for Bayesian Optimization in Materials Discovery
摘要
Bayesian optimization is widely used in active learning loops for materials science. Gaussian processes serve as the de-facto surrogate model family, but require task-dependent kernel choices and can lack the modeling capacity for complex relationships. Tabular foundation models (TFMs) have recently emerged as the state-of-the-art for predictive tasks on structured data, without requiring bespoke kernels or hyperparameter tuning. We evaluate two TFMs, TabPFN and TabICL, as out-of-the-box surrogate models on two standard materials science benchmarks, comparing against a large field of baselines encompassing Gaussian processes, random forests, and CatBoost. Our findings indicate that TFMs are highly competitive surrogate models across the two benchmarks, without task-specific training for active learning or GP kernel selection. Our results motivate developing TFMs further as surrogates for active learning in materials discovery.
作者备注：Accepted at the AI4Mat Workshop at NeurIPS 2026. 19 pages, 12 figures, 5 tables. Code: https://github.com/PriorLabs/matsci-bo-bench