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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Active Learning for Discovering Complex Phase Diagrams with Gaussian Processes

Max Zhu · Jian Yao · Marcus Mynatt · Hubert Pugzlys · Shuyi Li · Qingyuan Zhao · Chunjing Jia


We introduce a Bayesian active learning algorithm that efficiently elucidates phase diagrams. Using a novel acquisition function that assesses both the impact and likelihood of the next observation, the algorithm iteratively determines the most informative next experiment to conduct and rapidly discerns the phase diagrams with multiple phases. Comparative studies against existing methods highlight the superior efficiency of our approach. We demonstrate the algorithm's practical application through the successful identification of a skyrmion phase diagram.

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