Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning

• URL: http://arxiv.org/abs/2411.08414v1
• Date: Wed, 13 Nov 2024 08:07:21 GMT
• Title: Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning
• Authors: Chao Huang, Chunyan Chen, Ling Shi, Chen Chen,
• Abstract summary: We build an element property knowledge graph and utilize an embedding model to encode the element attributes within the knowledge graph. A multimodal fusion framework, ESNet, integrates element property features with crystal structure features to generate joint multimodal representations. This provides a more comprehensive perspective for predicting the performance of crystalline materials.
• Score: 8.523289773617503
• License:
• Abstract: Machine learning has become a crucial tool for predicting the properties of crystalline materials. However, existing methods primarily represent material information by constructing multi-edge graphs of crystal structures, often overlooking the chemical and physical properties of elements (such as atomic radius, electronegativity, melting point, and ionization energy), which have a significant impact on material performance. To address this limitation, we first constructed an element property knowledge graph and utilized an embedding model to encode the element attributes within the knowledge graph. Furthermore, we propose a multimodal fusion framework, ESNet, which integrates element property features with crystal structure features to generate joint multimodal representations. This provides a more comprehensive perspective for predicting the performance of crystalline materials, enabling the model to consider both microstructural composition and chemical characteristics of the materials. We conducted experiments on the Materials Project benchmark dataset, which showed leading performance in the bandgap prediction task and achieved results on a par with existing benchmarks in the formation energy prediction task.

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