Physics-informed Gaussian process classification for constraint-aware alloy design

Christofer Hardcastle

doi:10.1039/D5DD00084J

Physics-informed Gaussian process classification for constraint-aware alloy design

2025-07-26

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Christofer Hardcastle

DOI：10.1039/D5DD00084J

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Alloy design can be framed as a constraint-satisfaction problem. Building on previous methodologies; we propose equipping Gaussian Process Classifiers (GPCs) with physics-informed prior mean functions to model the centers of feasible design spaces. Through three case studies; we highlight the utility of informative priors for handling constraints on continuous and categorical properties. (1) Phase stability: by incorporating CALPHAD predictions as priors for solid-solution phase stability; we enhance model validation using a publicly available XRD dataset. (2) Phase stability prediction refinement: we demonstrate an in silico active learning approach to efficiently correct phase diagrams. (3) Continuous property thresholds: by embedding priors into continuous property models; we accelerate the discovery of alloys meeting specific property thresholds via active learning. In each case; integrating physics-based insights into the classification framework substantially improved model performance; demonstrating an efficient strategy for constraint-aware alloy design.

Keywords:

Gaussian Process Classifiers

physics-informed priors

alloy design

constraint-satisfaction problem

active learning

Physics-informed Gaussian process classification for constraint-aware alloy design

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