利用Kolmogorov-Arnold网络高效预测势能面与物理性质
Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks
AI总结：
本研究在Allegro、NequIP和ETGNN三个机器学习框架中用KANs替代MLPs，证明KANs能提高预测精度和计算效率，尤其在输出模块中应用效果最佳，展示了KANs在材料性质预测中的潜力。
摘要：
机器学习方法在材料科学中的性质预测应用已引起广泛关注。在近期进展中，Kolmogorov-Arnold Networks（KANs）已成为传统多层感知器（MLPs）的一种有前景的替代方案。本研究评估了在三个已建立的机器学习框架中用KANs替代MLPs的影响，这三个框架分别是：Allegro、神经等变原子间势（NequIP）以及基于边的张量预测图神经网络（ETGNN）。我们的结果表明，KANs的集成通常能带来更高的预测精度。具体而言，在输出模块中用KANs替代MLPs可显著提高精度，并且在某些情况下还能缩短训练时间。此外，与在整个模型中全部使用KANs相比，仅在输出模块中使用KANs有助于加快推理速度并提高计算效率。研究发现，KANs最优基函数的选择取决于所处理的具体问题。我们的结果证明了KANs在增强机器学习势和材料性质预测方面的强大潜力。
英文摘要：
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.
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