COMBO: An efficient Bayesian optimization library for materials science
Abstract In many subfields of chemistry and physics, numerous attempts have been made to accelerate scientific discovery using data driven experimental design algorithms. Among them, Bayesian optimization has been proven to be an effective tool. A standard implementation (e.g. scikit learn) however, can accommodate only small training data. We designed an efficient protocol for Bayesian optimization that employs Thompson sampling, random feature maps, one rank Cholesky update and automatic hyperparameter tuning, and implemented it as an open source python library called COMBO (COMmon Bayesian Optimization library) Promising results using COMBO to determine the atomic structure of a crystalline interface are presented. COMBO is available at https:/github.com/tsudalab/combo