KAGNN模型在无机纳米材料预测中表现卓越，显著超越传统GNN方法。
Kolmogorov-Arnold graph neural networks for chemically informed prediction tasks on inorganic nanomaterials
- 基于柯尔莫哥洛夫-阿诺德网络构建新型图神经网络，提升化学信息建模能力。
- 在CHILI-3K数据集上，晶系与空间群分类准确率分别达99.5%和96.6%，创历史新高。
- 特别适合需要高精度化学结构解析的无机纳米材料研究者使用。
Kolmogorov-Arnold Networks (KANs) 近年来被引入图神经网络（GNN）领域，尤其在分子数据建模与药物发现中取得进展。本文提出的柯尔莫哥洛夫-阿诺德图神经网络（KAGNN）拓展了基于KAN的GNN模型体系。此前，该类模型已在有机分子性质预测任务中超越基于MLP的GNN。本研究首次将KAGNN应用于无机纳米材料，基于2024年发布的大型数据集CHILI（Chemically-Informed Large-scale Inorganic Nanomaterials Dataset），针对8项预定义任务进行适配与测试。实验表明，KAGNN在多数任务中优于对应的传统GNN模型。尤其在CHILI-3K数据集的晶系与空间群分类任务中，分别达到99.5%与96.6%的准确率，显著超越此前65.7%与73.3%的水平，创下新纪录。
原文摘要 · Abstract (English)
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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