Bayesian optimization for materials design
This work addresses the challenge of reducing experimental costs in materials science, presenting an incremental application of existing Bayesian optimization methods to a specific domain.
The paper tackles the problem of optimizing materials design by introducing Bayesian optimization to guide experiments and find good designs efficiently, focusing on low-dimensional parameterizations and using Gaussian process regression for prediction.
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality.