SPEAR: Structure Property Explainability with Attention Regularization
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 a...
| Authors | Aditya Raghavan, Utkarsh Pratiush, Dalton A. Pearl et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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