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Life sciences · Preprint
arXiv · August 13, 2026
Early or partial results. Treat as a signal, not a conclusion.
SPEAR is a machine learning framework that regularizes attention mechanisms to improve interpretability of structure–property predictions in materials discovery. On synthetic data with known ground truth, regularized attention produces smooth, physically interpretable attribution patterns; applied to experimental rare-earth zirconate thin-film X-ray diffraction data, it identified a relationship between a 220 diffraction peak and tetragonal distortion affecting thermal conductivity. This is an early-stage methodological contribution without peer review, comparison to alternative explainability methods, or independent external validation.
Methods development with synthetic and experimental validation. Synthetic spectral data with known structure; experimental X-ray diffraction data from a combinatorial rare-earth zirconate thin-film library. Intervention: SPEAR framework with learnable temperature and smoothness penalty regularization applied to attention-based regression models.
Attention regularization with learnable temperature and smoothness penalty produced smooth, contiguous attribution profiles aligned with causal features on synthetic spectral benchmarks Applied to experimental X-ray diffraction data, the regularized model selectively emphasized physically relevant diffraction features and decoupled feature importance from raw peak intensity A previously overlooked 220 diffraction peak was identified, correlating with tetragonal distortion and local thermal conductivity in the rare-earth zirconate thin-film library
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A methodological proof-of-concept demonstrating an attention regularization framework on synthetic benchmarks and one experimental materials dataset, without independent validation, performance comparison to established methods, or peer review.
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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.
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