Bayesian Active Learning to Accelerate High Throughput Phase Diagram Exploration

Mingzhou Fan; Yucheng Wang; Guillermo Vazquez; Ruida Zhou; İbrahim Karaman; Raymundo Arroyave; Xiaoning Qian

doi:10.1039/d5dd00459d

January 1, 2026Digital DiscoveryOpen Access

Bayesian Active Learning to Accelerate High Throughput Phase Diagram Exploration

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Key Points

This research aims to enhance the efficiency of constructing high-dimensional phase diagrams using advanced computational techniques.
Implemented Bayesian active learning methods to optimize phase diagram exploration
Utilized high throughput calculations for rapid data generation
Evaluated performance against traditional CALPHAD methods
Achieved significant reduction in computation time for phase diagram construction
Improved accuracy of phase stability predictions
Facilitated the discovery of new materials more efficiently

Implication

Demonstrates improved phase diagram exploration using Bayesian active learning for material synthesis, implying enhanced efficiency.

Abstract

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....

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Cite This Study

Fan et al. (2026) studied this question.

synapsesocial.com/papers/6a23bcab71a5da9775e77cac https://doi.org/10.1039/d5dd00459d

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Bayesian Active Learning to Accelerate High Throughput Phase Diagram Exploration

Key Points

Implication

Abstract

Discussion

Cite This Study

Bayesian Active Learning to Accelerate High Throughput Phase Diagram Exploration

Key Points

Implication

Abstract

Discussion

Cite This Study

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