AI RESEARCH
Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks
arXiv CS.LG
•
ArXi:2604.04636v1 Announce Type: cross 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