Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

2026/01/11 by Bin Cao, Jie Xiong, Jiaxuan Ma +11 · 1 voice · 3 citations
Physics and Astronomy · #cond-mat.mtrl-sci

paper · pdf · doi:10.1038/s41524-026-02226-3

arxiv published 2026/01/11 · arxiv updated 2026/07/09

Abstract

Efficient exploration of vast compositional and processing spaces remains a major challenge in accelerated materials discovery. Bayesian optimization (BO) provides a principled approach to identify optimal materials with minimal experimentation, but its adoption has been limited by implementation complexity and a lack of domain-specific tools. Here, we present Bgolearn, a versatile Python framework that brings BO to materials research through intuitive interfaces, robust algorithms, and materials-focused workflows. Bgolearn supports single- and multi-objective optimization, multiple acquisition strategies, diverse surrogate models, and uncertainty quantification, enabling effective navigation of complex design spaces. Benchmark studies show that Bgolearn reduces experimental effort by 40-60% compared with random search, grid search, and genetic algorithms, while achieving comparable or superior solution quality. Its effectiveness is demonstrated across case studies, including the discovery of maximum-elastic-modulus triply periodic minimal surface structures, ultra-high-hardness high-entropy alloys, and high-strength, high-ductility medium-Mn steels, and is further supported by numerous publications. With a modular architecture that integrates seamlessly into existing materials workflows and a graphical interface (BgoFace) that removes programming barriers, Bgolearn establishes a practical, reliable platform for Bayesian optimization in materials science. The software is openly available at https://github.com/Bin-Cao/Bgolearn.

Citations

• Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
• Increasing certainty in systems biology models using Bayesian multimodel inference
• A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
• Bayesian Optimization Algorithms for Accelerator Physics
• AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
• Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
• Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
• Bayesian reaction optimization as a tool for chemical synthesis
• A mobile robotic chemist
• Self-driving laboratory for accelerated discovery of thin-film materials
• High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates
• Scikit-learn: Machine Learning in Python

Discussions

• Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery https://arxiv.org/abs/2601.06820 [bsky, 0 points, 0 comments]

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