Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
This work addresses constraint-aware alloy design for materials scientists, offering incremental improvements by integrating physics-based insights into existing classification frameworks.
The paper tackled alloy design as a constraint-satisfaction problem by equipping Gaussian Process Classifiers with physics-informed prior mean functions to model feasible design spaces, resulting in substantially improved model performance across three case studies, such as enhancing validation with a public XRD dataset and accelerating alloy discovery via active learning.
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 boundaries 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.