Interpretable Bayesian Optimization for Catalyst Discovery

Abstract

Bayesian Optimization (BO) efficiently explores vast design spaces using probabilistic surrogate models, enabling the guided discovery of materials with desired properties. However, most BO frameworks rely on the knowledge of a few key physical parameters (features) correlated with the materials property of interest. This is a challenge in heterogeneous catalysis, where the material properties are governed by an intricate interplay of multiple physical processes, and the mentioned parameters are typically unknown. Here, we introduce the Sparse Adaptive Representation–based Bayesian Optimization (SARBO) framework that utilizes the sure independence screening and sparsifying operator (SISSO) symbolic-regression method for on-the-fly selection of key physical parameters correlated with materials properties during BO. Crucially, SISSO takes into account nonlinear relationships and interactions between multiple parameters when selecting key features. We demonstrate that SARBO enables efficient navigation of the materials spaces and outperforms widely used feature-selection approaches for the simulated discovery of single- and dual-atom alloy surface sites capable of activating CO₂, a critical step in the O₂ reduction reaction.