Bayesian Active Learning to Accelerate High Throughput Phase Diagram Exploration

Mingzhou Fan

doi:10.1039/D5DD00459D

Bayesian Active Learning to Accelerate High Throughput Phase Diagram Exploration

2026-06-05

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DOI：10.1039/D5DD00459D

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Phase diagrams are fundamental for understanding phase stability and guiding the synthesis of new materials. However; constructing high-dimensional phase diagrams through exhaustive CALPHAD (CALculation of PHAse Diagrams) computations remains costly. We introduce a Bayesian Active Learning for Phase Diagram Discovery (BALPI) framework that efficiently identifies phase stability regions by adaptively sampling the thermodynamic space using uncertainty-aware acquisition strategies. BALPI integrates Gaussian Process Classifiers and Regressors within two complementary formulations-classification and level-set estimation-and introduces non-myopic Bayesian acquisition functions; including the Soft Mean Objective Cost of Uncertainty (SMOCU) and an extended straddle (estraddle) criterion. Using CALPHAD-based phase stability predictions as the ground-truth oracle; BALPI achieves accurate reconstruction of phase boundaries with significantly fewer queries than conventional label propagation and label spreading baselines. Results on SiO2-Al2O3-MgO and Ni-Ti-Hf-Cu systems demonstrate that BALPI captures disconnected phase regions and achieves consistent reductions in Bayesian error and computational cost. More importantly; this work establishes BALPI as a general framework for uncertainty-guided phase diagram discovery and highlights the potential of Bayesian active learning to accelerate computational thermodynamics and materials design; through the efficient exploration of the phase stability landscape at much lower costs relative to competing strategies.

Bayesian Active Learning to Accelerate High Throughput Phase Diagram Exploration

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