Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design

TL;DR

This paper introduces a physics-informed Gaussian Process Classification method that incorporates domain knowledge to improve constraint-aware alloy design, enabling more efficient exploration of feasible alloy spaces.

Contribution

It develops a novel approach that integrates physics-based priors into Gaussian Process Classifiers for better modeling of alloy design constraints.

Findings

Enhanced model validation with CALPHAD-based priors

Efficient in silico active learning for phase diagram correction

Accelerated alloy discovery through property threshold modeling

Abstract

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.