Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks

Abstract

The application of machine learning methods for predicting potential energy surface and physical properties within materials science has garnered significant attention. Among recent advancements, Kolmogorov-Arnold Networks (KANs) have emerged as a promising alternative to traditional Multi-Layer Perceptrons. This study evaluates the impact of substituting Multi-Layer Perceptrons with KANs within four established machine learning frameworks: Allegro, Neural Equivariant Interatomic Potentials, Higher Order Equivariant Message Passing Neural Network (MACE), and the Edge-Based Tensor Prediction Graph Neural Network. Our results demonstrate that the integration of KANs enhances prediction accuracies, especially for complex datasets such as the HfO2 structures. Notably, using KANs exclusively in the output block achieves the most significant improvements, improving prediction accuracy and computational efficiency. Furthermore, employing KANs exclusively in the output block facilitates faster inference and improved computational efficiency relative to utilizing KANs throughout the entire model. The selection of optimal basis functions for KANs depends on the specific problem. Our results demonstrate the strong potential of KANs in enhancing machine learning potentials and material property predictions. Additionally, the proposed methodology offers a generalizable framework that can be applied to other ML architectures.