Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks
Characterizing crystalline energy landscapes is essential to predicting thermodynamic stability, electronic structure, and functional behavior. While machine learning (ML) enables rapid property predictions, the "black-box" nature of most models limits their utility for generating new scientific insights. Here, we introduce Kolmogorov-Arnold Networks (KANs) as an interpretable framework to bridge this gap. Unlike conventional neural networks with fixed activation functions, KANs employ learnable functions that reveal underlying physical relationships. We developed the Element-Weighted KAN, a composition-only model that achieves state-of-the-art accuracy in predicting formation energy, ban...
| Authors | Gen Zu, Ning Mao, Claudia Felser et al. |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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