[2604.04636] Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks

Condensed Matter > Disordered Systems and Neural Networks arXiv:2604.04636 (cond-mat) [Submitted on 6 Apr 2026] Title:Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks Authors:Gen Zu, Ning Mao, Claudia Felser, Yang Zhang View a PDF of the paper titled Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks, by Gen Zu and 3 other authors View PDF HTML (experimental) Abstract: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, band gap, and work function across large-scale datasets. Crucially, without any explicit physical constraints, KANs uncover interpretable chemical trends aligned with the periodic table and quantum mechanical principles through embedding analysis, correlation studies, and principal component analysis. These results demonstrate that KANs provide a powerful framework with high pr...