2 papers
physics.chem-ph2026
Non-covalent Interactions at cm Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials
Yulin Shen, Shahzad Akram, Louis Primeau +4
Foundation models in atomistic machine learning encode interaction physics across diverse atomic environments, but whether that structure can be transferred when building specialis…
cond-mat.dis-nn2026
Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks
Gen Zu, Ning Mao, Claudia Felser +1
Characterizing crystalline energy landscapes is essential to predicting thermodynamic stability, electronic structure, and functional behavior. While machine learning (ML) enables…