CTGNN: Crystal Transformer Graph Neural Network for Crystal Material Property Prediction

2024/05/19 by Zijian Du, Du, Zijian, Luozhijie Jin +11 · 2 citations
Engineering · Materials Science · #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Welding Techniques and Residual Stresses

paper · pdf · doi:10.48550/arxiv.2405.11502

openalex publication_date 2024/05/19 · openalex created_date 2024/05/22 · openalex updated_date 2026/07/28

Abstract

The combination of deep learning algorithm and materials science has made significant progress in predicting novel materials and understanding various behaviours of materials. Here, we introduced a new model called as the Crystal Transformer Graph Neural Network (CTGNN), which combines the advantages of Transformer model and graph neural networks to address the complexity of structure-properties relation of material data. Compared to the state-of-the-art models, CTGNN incorporates the graph network structure for capturing local atomic interactions and the dual-Transformer structures to model intra-crystal and inter-atomic relationships comprehensively. The benchmark carried on by the proposed CTGNN indicates that CTGNN significantly outperforms existing models like CGCNN and MEGNET in the prediction of formation energy and bandgap properties. Our work highlights the potential of CTGNN to enhance the performance of properties prediction and accelerates the discovery of new materials, particularly for perovskite materials.

Cited by

• Synthesizability Prediction of Crystalline Structures with a Hierarchical Transformer and Uncertainty Quantification
• Perovskite-R1: A Domain-Specialized LLM for Intelligent Discovery of Precursor Additives and Experimental Design

Related

• Graph Neural Networks Based Deep Learning for Predicting Structural and Electronic Properties
• MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
• Deep learning in-depth analysis of crystal graph convolutional neural networks: A new era in materials discovery and its applications
• CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties