CatBOX: A Categorical-Continuous Bayesian Optimization with Spectral Mixture Kernels for Accelerated Catalysis Experiments

TL;DR

CatBOX is a Bayesian Optimization method that efficiently finds optimal catalyst compositions and reaction conditions by jointly optimizing categorical and continuous parameters using spectral mixture kernels, demonstrated on synthetic and real catalytic experiments.

Contribution

Introduces a novel spectral mixture kernel and trust-region approach for joint optimization of categorical and continuous parameters in catalytic experiments, with theoretical verification and practical benchmarking.

Findings

Achieved over 3-fold improvement over baseline in synthetic benchmarks.

Achieved 19-fold improvement over random search.

Successfully identified top catalyst recipes in real experiments.

Abstract

Identifying optimal catalyst compositions and reaction conditions is central in catalysis research, yet remains challenging due to the vast multidimensional design spaces encompassing both continuous and categorical parameters. In this work, we present CatBOX, a Bayesian Optimization method for accelerated catalytic experimental design that jointly optimizes categorical and continuous experimental parameters. Our approach introduces a novel spectral mixture kernel that combines the inverse Fourier transform of Gaussian and Cauchy mixtures to provide a flexible representation of the continuous parameter space, capturing both smooth and non-smooth variations. Categorical choices, such as catalyst compositions and support types, are navigated via trust regions based on Hamming distance. For performance evaluation, CatBOX was theoretically verified based on information theory and benchmarked on a series of synthetic functions, achieving more than a 3-fold improvement relative to the best-performing baseline and a 19-fold improvement relative to random search on average across tasks. Additionally, three real-life catalytic experiments, including oxidative coupling of methane, urea-selective catalytic reduction, and direct arylation of imidazoles, were further used for in silico benchmarking, where CatBOX reliably identified top catalyst recipes and reaction conditions with the highest efficiencies in the absence of any a priori knowledge. Finally, we develop an open-source, code-free online platform to facilitate trial deployment in real experimental settings, particularly for self-driving laboratory environments.