bayesian-optimization

Bayesian Optimization for Materials and Chemistry

Description

This skill covers Bayesian optimization (BO) for materials discovery and chemistry design: surrogate model construction, acquisition function selection and optimization, batch and constrained BO, multi-objective Pareto-front search, multi-fidelity optimization, and integration with high-throughput DFT, experimental workflows, and active learning loops. It uses BoTorch and Ax as the primary frameworks. Invoke this skill when sequentially selecting experiments or calculations to optimize an expensive objective — band gap, formation energy, synthesis yield, reaction rate, or any property accessible via DFT, simulation, or laboratory measurement — especially when the budget is small (tens to low hundreds of evaluations) and uncertainty-guided exploration matters.

Domain Context

Bayesian optimization is a sequential decision-making strategy for optimizing an expensive black-box function f(x) using as few evaluations as possible. It maintains a probabilistic surrogate model p(f | data) — typically a Gaussian process — and selects the next evaluation point by maximizing an acquisition function α(x) that balances exploration (high uncertainty) and exploitation (high predicted value). After each evaluation, the surrogate is updated and the process repeats.

Why BO for materials: Materials property spaces are high-dimensional, discontinuous, combinatorially large, and expensive to evaluate. A DFT calculation costs minutes to days; a synthesis-and-characterization cycle costs weeks. Random or grid search wastes budget on obviously poor candidates. BO uses all past evaluations to build a model of the objective landscape and targets the most promising regions. In practice, BO routinely outperforms random search on materials benchmarks at budgets of 20–200 evaluations, though the advantage shrinks as budget grows. [EXPERT REVIEW NEEDED: the relative advantage of BO over random search is problem-specific and sometimes smaller than reported in clean benchmarks; noisy experimental settings often reduce the margin]