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.

Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

“…ML-driven experiments tend to identify promising materials much more efficiently than a naive search. 19 Several implementations of ML-optimized experimental design have emerged, including COMBO, 20 FUELS, 19 and work from Xue et al 18 …”

Machine Learning-Based Experimental Design in Materials Science

“…We implemented an open source package for Bayesian optimization in python (COMBO: COMmon Bayesian Optimization library, https://github.com/tsudalab/ combo) [11]. Thompson sampling, random feature maps and one-rank Cholesky update made it particularly suitable to handle large training datasets.…”

“…To investigate the efficiency of MDTS, we compared the application of MDTS and an efficient Bayesian optimization package [11] to design optimal SiliconGermanium (Si-Ge) alloy interfacial structures (Si:Ge = 1:1) in order to achieve both minimum and maximum thermal conductance [7]. The total computation time was Algorithm 1: Monte Carlo tree search divided into design time and simulation time.…”

“…The first is Bayesian optimization (BO) [9], which has been proven effective in many materials design and discovery studies [1,2,6,7,[10][11][12][13]. In BO methods, a machine learning model is employed to reconstruct the black-box function f (s).…”

Atomic-Scale Nanostructures by Advanced Electron Microscopy and Informatics

“…However, many trial calculations are still necessary to determine a single grain boundary structure. More recently, much more efficient methods based on machine learning techniques, including virtual screening and Bayesian optimization have been proposed by the present authors [12,[15][16][17]. Those methods are described below.…”