Application of Bayesian Optimization and Regression Analysis to Ferromagnetic Materials Development

Bayesian optimization (BO) is a well-developed machine learning (ML) field for black-box function optimization. In BO, a surrogate predictive model, here a Gaussian process, is used to approximate the black-box function. The estimated mean and uncertainty of the surrogate model are paired with an acquisition function to decide where to sample next. In this study, we applied this technique to known ferromagnetic thin-film materials such as ferromagnetic (Fe _100−y Ga y ) _1−x B _x ( x = 0−21 and y = 9−17) to demonstrate optimization of structure–property relationships, specifically the dopant concentration or stoichiometry effect on magnetostriction and ferromagnetic resonance linewidth. Our results demonstrated that BO can be deployed to optimize structure–property relationships in FeGaB and FeGaC thin films. We have shown through simulation that using BO methods to guide experiments reduced the number of samples required to statistically determine the maximum or minimum by 50% compared to traditional methods. Our results suggest that BO can be used to save time and resources to optimize ferromagnetic films. This method is transferrable to other ferromagnetic material structure–property relationships, providing an accessible implementation of ML to magnetic materials development.