Title: Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks Show Chinese title

Title: 用Kolmogorov-Arnold网络高效预测势能面和物理性质

Abstract: The application of machine learning methodologies for predicting 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 (MLPs). This study evaluates the impact of substituting MLPs with KANs within three established machine learning frameworks: Allegro, Neural Equivariant Interatomic Potentials (NequIP), and the Edge-Based Tensor Prediction Graph Neural Network (ETGNN). Our results demonstrate that the integration of KANs generally yields enhanced prediction accuracies. Specifically, replacing MLPs with KANs in the output blocks leads to notable improvements in accuracy and, in certain scenarios, also results in reduced training times. 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 an optimal basis function for KANs is found to be contingent upon the particular problem at hand. Our results demonstrate the strong potential of KANs in enhancing machine learning potentials and material property predictions. Show Chinese abstract

Abstract: 机器学习方法在材料科学中预测性质的应用已经引起了广泛关注。 在最近的进展中，Kolmogorov-Arnold网络（KANs）作为一种传统多层感知器（MLPs）的有前途的替代方案出现了。 本研究评估了在三个已建立的机器学习框架：Allegro、神经等变原子间势（NequIP）和基于边的张量预测图神经网络（ETGNN）中用KANs替换MLPs的影响。 我们的结果表明，集成KANs通常会提高预测准确性。 具体而言，在输出块中用KANs替换MLPs会导致准确性的显著提升，并在某些情况下还会减少训练时间。 此外，仅在输出块中使用KANs相比在整个模型中使用KANs能实现更快的推理和更高的计算效率。 选择适合KANs的最佳基函数取决于具体的问题。 我们的结果展示了KANs在增强机器学习势和材料性质预测方面的巨大潜力。