Physics-informed acquisition weighting for stoichiometry-constrained Bayesian optimization of oxide thin-film growth

TL;DR

This paper introduces a physics-informed Bayesian optimization method that incorporates physical priors into the acquisition function, enabling efficient and rapid optimization of oxide thin-film growth by steering the search toward physically plausible conditions.

Contribution

The method modifies the acquisition function with a physics-based weighting, improving the efficiency of Bayesian optimization in materials synthesis without complex model changes.

Findings

Achieved convergence of LaAlO3 lattice constant within 15 growth runs

Steered optimization toward stoichiometric regions while allowing exploration

Demonstrated rapid and efficient optimization in oxide thin-film growth

Abstract

We present a physics-informed Bayesian optimization (PIBO) with a concise modification to its acquisition function to incorporate the physical prior knowledge. Specifically, this method multiplies the expected improvement (EI) by a weight encoding prior crystal growth physics. When applied to LaAlO3 molecular-beam epitaxy, the weighting function defines a flat stoichiometric window and penalizes off-window proposals, thereby steering the optimization toward physically plausible regions while maintaining controlled exploration. In a closed-loop optimization, relative to the bare EI, which often proposes off-stoichiometric conditions, the weighted EI constrains the search toward stoichiometric regions while retaining sufficient flexibility to explore neighboring conditions, eventually identifying an optimum slightly beyond the stoichiometric window. Within only 15 growth runs, the lattice constant of the grown LaAlO3 film converged to the bulk value, evidencing efficient and rapid optimization for the ideal stoichiometric growth. Because physics knowledge is incorporated solely through the weighting function, the approach requires only minimal modification to standard BO workflows and is readily applicable to other material systems, offering a general and practical route to AI-driven materials synthesis.