High-Dimensional Discrete Bayesian Optimization with Self-Supervised Representation Learning for Data-Efficient Materials Exploration

A material exploration model based on high-dimensional discrete Bayesian optimization is introduced. Features were extracted from a large-scale database of ab-initio calculations by self-supervised representation learning. Material exploration was carried out based on 100 prior target values from 6,218 candidate materials. As a baseline, ten human experts of materials science were selected and evaluated their exploration efficiency. Under the same conditions, the proposed discrete algorithm was 1.93 times as efficient as human experts on average, while the conventional continuous algorithm could not outperform them.