Sparse modeling based Bayesian optimization for experimental design

Abstract

The efficient optimization of high-dimensional synthesis parameters using Bayesian optimization (BO) remains challenging in recent materials exploration. Sparse-modeling-based BO is a powerful method for optimizing high-dimensional synthesis parameters. However, previous methods, such as BO using an automatic relevance determination kernel, cannot explore various materials. This study proposes a sparse modeling-based BO using the maximum partial dependence effect (MPDE). The optimization performances of the proposed and conventional BOs are compared using model functions to simulate materials synthesis in a high-dimensional search space. Compared to conventional sparse estimation methods using ARD kernels, Bayesian optimization with MPDE allows the materials researcher to easily set the threshold for sparse estimation, leading to optimization with fewer trials. This is because, in MPDE, the scale of threshold is the same as the materials' properties, allowing an intuitive setting to ignore synthetic parameters that affect, for example, only up to 10% of the target value at most. Therefore, BO using MPDE is expected to facilitate the exploration of materials in a high-dimensional search space and is applicable to automatic and autonomous experiments utilizing robots.