用物理先验增强高斯过程分类，提升合金设计的约束满足效率
Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
- 将CALPHAD等物理预测作为先验嵌入高斯过程分类器
- 在三个案例中显著提升相稳定性和性能阈值预测精度
- 适合材料设计与主动学习研究者快速定位可行合金配方
合金设计可视为约束满足问题。本文提出将物理信息先验均值函数引入高斯过程分类器（GPC），以建模可行设计空间边界。通过三个案例验证：(1) 利用CALPHAD预测作为固溶相稳定性的先验，结合公开的XRD数据集提升模型验证效果；(2) 采用模拟主动学习方法高效修正相图；(3) 在连续性质模型中嵌入先验，借助主动学习加速寻找满足特定性能阈值的合金。实验表明，将物理知识融入分类框架能显著提升模型性能，为约束感知合金设计提供高效策略。
原文摘要 · Abstract (English)
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.
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