Graph-Transformer Model for Direct Band Structure Prediction from Crystal Structures

Predicting electronic band structures from crystal structures is essential for efficient materials discovery and design. While previous machine learning models used crystal graph neural networks for single-valued property predictions (such as CGCNN and MEGNet) and (equivariant) graph convolutional neural networks for Hamiltonian estimates (such as DeepH and DeepH-E3), our work introduces a novel approach to learn electronic structures directly from atomic crystal structures. We developed the first end-to-end model that directly predicts band structures from crystal structures. The model combines a crystal graph transformer as an encoder to capture the complex patterns within the crystal and a graph2seq layer as a decoder to convert the encoded crystal information into a sequential representation of the electronic band structures. Our results showcase the ability to accurately predict two bands close to the Fermi level for each crystal. Beyond its current application, our framework can be extended to predict additional energy bands and large-scale crystal systems, offering a faster alternative to traditional density functional theory calculations and enhancing the efficiency of large-scale functional materials discovery and machine learning tasks.