The recent development of Kolmogorov-Arnold Networks (KANs) has found its application in the field of Graph Neural Networks (GNNs) particularly in molecular data modeling and potential drug discovery. Kolmogorov-Arnold Graph Neural Networks (KAGNNs) expand on the existing set of GNN models with KAN-based counterparts. KAGNNs have been demonstrably successful in surpassing the accuracy of MultiLayer Perceptron (MLP)-based GNNs in the task of molecular property prediction. These models were widely tested on the graph datasets consisting of organic molecules. In this study, we explore the application of KAGNNs towards inorganic nanomaterials. In 2024, a large scale inorganic nanomaterials dataset was published under the title CHILI (Chemically-Informed Large-scale Inorganic Nanomaterials Dataset), and various MLP-based GNNs have been tested on this dataset. We adapt and test our own KAGNNs appropriate for eight defined tasks. Our experiments reveal that, KAGNNs frequently surpass the performance of their counterpart GNNs. Most notably, on crystal system and space group classification tasks in CHILI-3K, KAGNNs achieve the new state-of-the-art results of 99.5 percent and 96.6 percent accuracy, respectively, compared to the previous 65.7 and 73.3 percent each.

翻译：Kolmogorov-Arnold网络（KANs）的最新发展已在图神经网络（GNNs）领域，特别是在分子数据建模和潜在药物发现中得到了应用。Kolmogorov-Arnold图神经网络（KAGNNs）在现有GNN模型基础上扩展了基于KAN的对应架构。在分子性质预测任务中，KAGNNs已被证明能够显著超越基于多层感知机（MLP）的GNNs的准确率。这些模型已在包含有机分子的图数据集上进行了广泛测试。在本研究中，我们探索了KAGNNs在无机纳米材料中的应用。2024年，一个名为CHILI（化学信息大规模无机纳米材料数据集）的大规模无机纳米材料数据集发布，多种基于MLP的GNNs已在该数据集上进行了测试。我们针对八项定义的任务调整并测试了我们自有的KAGNNs。实验结果表明，KAGNNs在多数情况下优于对应的GNNs。最值得注意的是，在CHILI-3K的晶系和空间群分类任务中，KAGNNs分别取得了99.5%和96.6%准确率的最新最优结果，而此前的最佳准确率分别为65.7%和73.3%。