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DISTAL: Distillation and Self-Supervised Pretraining for Structure-Agnostic Materials Property Prediction

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled samples. Models with the strongest predictive accuracy often depend on crystal structures, which restricts their use in early-stage screening when structural information is limited or unavailable. To address this challenge, we propose DISTAL, a dual-prior framework for structure-agnostic materials property prediction that combines self-supervised compositional pretraining with structure-aware knowledge distillation. DISTAL first learns transferabl

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