Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

Efficient exploration of vast compositional and processing spaces is essential for accelerated materials discovery. Bayesian optimization (BO) provides a principled strategy for identifying optimal materials with minimal experiments, yet its adoption in materials science ishindered by implementation complexity and limited domain-specific tools. Here, we presentBgolearn, a comprehensive Python framework that makes BO accessible and practical for materials research through an intuitive interface, robust algorithms, and materials-oriented workflows. Bgolearn supports both single-objective and multi-objective Bayesian optimization withmultiple acquisition functions (e.g., expected improvement, upper confidence bound, probability of improvement, and expected hypervolume improvement etc.), diverse surrogate models(including Gaussian processes, random forests, and gradient boosting etc.), and bootstrapbased uncertainty quantification. Benchmark studies show that Bgolearn reduces the numberof required experiments by 40–60% compared with random search, grid search, and geneticalgorithms, while maintaining comparable or superior solution quality. Its effectiveness isdemonstrated not only through the studies presented in this paper, such as the identificationof maximum-elastic-modulus triply periodic minimal surface structures, ultra-high-hardnesshigh-entropy alloys, and high-strength, high-ductility medium-Mn steels, but also by numerous publications that have proven its impact in material discovery. With a modular architecture that integrates seamlessly into existing materials workflows and a graphical user interface (BgoFace) that removes programming barriers, Bgolearn establishes a practical andreliable platform for Bayesian optimization in materials science, and is openly available athttps://github.com/Bin-Cao/Bgolearn.