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Figure 1. Efficient prediction of potential energy surface and physical properties with KAN. (A) Comparison of MLP and KAN^[36]. MLPs utilize learnable weights on the edges and fixed activation functions on nodes. In contrast, KANs employ learnable activation functions parameterized as various basis functions on edges with sum operations on nodes; (B) Replacing MLPs in ML potentials and property prediction models with KANs. The left side illustrates the general framework of ML potentials and property prediction models. In this study, MLPs in different parts of the ML potentials and property prediction models are replaced with KANs employing various basis functions. Our results demonstrate that replacing MLPs with KANs in the output blocks leads to higher prediction accuracy and reduced training times compared to using MLPs, and higher inference speed and computation resource efficiency compared to using KANs without MLPs. MLPs: Multi-layer perceptrons; KANs: Kolmogorov-Arnold Networks; ML: Machine learning.