Kolmogorov-Arnold图神经网络用于无机纳米材料的化学信息预测任务
Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials
AI总结：
KAGNNs在无机纳米材料的化学信息预测任务中表现出色，尤其在晶系和空间群分类任务中达到新高准确率。
摘要：
最近Kolmogorov-Arnold网络（KANs）的发展在图神经网络（GNN）领域取得了应用，特别是在分子数据建模和潜在药物发现领域。Kolmogorov-Arnold图神经网络（KAGNNs）扩展了现有的GNN模型集，加入了基于KAN的对应模型。KAGNNs在分子属性预测任务中已证明在准确率上超过了基于多层感知机（MLP）的GNNs。这些模型在包含有机分子的图数据集上被广泛测试。在本研究中，我们探讨了KAGNNs在无机纳米材料中的应用。2024年，一个大规模的无机纳米材料数据集以“CHILI（化学信息大规模无机纳米材料数据集）”为标题发布，各种基于MLP的GNNs已在该数据集上进行了测试。我们适应并测试了适用于八个定义任务的自己的KAGNNs。我们的实验表明，KAGNNs在性能上经常超过其对应的GNNs。最值得注意的是，在CHILI-3K的晶系和空间群分类任务中，KAGNNs分别达到了99.5%和96.6%的准确率，这比之前的65.7%和73.3%有了显著提升。
英文摘要：
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.
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