发表机构
University of Tennessee（田纳西大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SPEAR框架，通过训练时约束注意力分布提升结构-性质回归模型的可解释性，在合成及实验数据上验证其能输出物理意义明确的归因，且不损失预测性能。
AI 中文摘要
机器学习越来越多地用于从光谱和衍射数据中学习结构-性质关系，但其在材料发现中的应用往往受到模型预测可解释性差的限制。尽管注意力机制常被视为具有内在可解释性，但未正则化的注意力可能产生不稳定、碎片化或受强度驱动的归因模式，掩盖这些关系的物理起源。本文提出SPEAR（Structure Property Explainability with Attention Regularization，结合注意力正则化的结构-性质可解释性框架），该框架在训练过程中约束注意力分布，以提升其稳定性、选择性和物理可解释性。SPEAR在基于注意力的回归中加入可学习温度（控制注意力集中度）和平滑惩罚项（强制相邻光谱位置的一致性），将注意力视为可学习的解释对象而非事后可视化。通过具有已知生成结构的合成光谱基准测试，结果显示注意力正则化可产生与因果特征对齐的平滑连续归因轮廓，同时保持预测准确性。将其应用于组合稀土锆酸盐薄膜库的实验X射线衍射数据时，正则化模型选择性强调物理相关的衍射特征，并将特征重要性与原始峰强度解耦。其识别出的反射促使重新评估早期结构分析，揭示了220峰位置、容纳阳离子尺寸无序的四方畸变与局部热导率之间的相关性。因此，注意力正则化为可解释的结构-性质回归提供了有原则的训练约束，在不牺牲预测性能的情况下产生具有机制意义的解释。
英文摘要
Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions. Although attention mechanisms are frequently treated as inherently explainable, unregularized attention can yield unstable, fragmented, or intensity driven attribution patterns that obscure the physical origin of these relationships. Here we introduce SPEAR (Structure Property Explainability with Attention Regularization), a framework that constrains attention distributions during training to improve their stability, selectivity, and physical interpretability. SPEAR augments attention based regression with a learnable temperature that controls attention concentration and a smoothness penalty that enforces coherence across neighboring spectral positions, treating attention as a learnable explanatory object rather than a post hoc visualization. Using synthetic spectral benchmarks with known generative structure, we show that attention regularization produces smooth, contiguous attribution profiles aligned with causal features while preserving predictive accuracy. Applied to experimental X ray diffraction data from a combinatorial rare earth zirconate thin film library, the regularized model selectively emphasizes physically relevant diffraction features and decouples feature importance from raw peak intensity. The reflection it identified prompted a reassessment of our earlier structural analysis, revealing a correlation between the 220 peak position, the tetragonal distortion that accommodates cation size disorder, and the local thermal conductivity. Attention regularization therefore provides a principled training constraint for explainable structure property regression, yielding mechanistically meaningful explanations without sacrificing predictive performance.
