Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
Can Polat, Hasan Kurban, Erchin Serpedin, and Mustafa Kurban
分子图神经网络(GNNs)通常只关注基于XYZ的几何表示,因此忽略了像PubChem这样的公共数据库中可用的有价值的化学上下文。 这项工作引入了一个多模态框架,该框架集成了文本描述符,如IUPAC名称,分子公式,物理化学特性和同义词,以及分子图。 门控融合机制平衡了几何和文本特征,允许模型利用互补信息。 对基准数据集的实验表明,添加文本数据对某些电子属性产生了显着的改进,而其他电子属性的收益仍然有限。 此外,GNN架构显示类似的性能模式(在类似目标上改进和恶化),表明它们学习可比的表示,而不是明显不同的物理见解。
Molecular graph neural networks (GNNs) often focus exclusively on XYZ-based geometric representations and thus overlook valuable chemical context available in public databases like PubChem. This work introduces a multimodal framework that integrates textual descriptors, such as IUPAC names, molecular formulas, physicochemical properties, and synonyms, alongside molecular graphs. A gated fusion mechanism balances geometric and textual features, allowing models to exploit complementary information...