4 papers
Point Group Equivariant Graph Neural Networks for Materials
Alexander J. Heilman, Qimin Yan
Equivariant graph neural networks have proven effective tools for inference of material's properties directly from their structure. Traditionally, these have been applied such that…
Materials design based on a material-motif network and heterogeneous graphs
Anoj Aryal, Weiyi Gong, Huta Banjade +1
Machine learning models for functional materials design require precise and informative representations of material systems. Common representations encode atomic composition and bo…
Graph Transformer Networks for Accurate Band Structure Prediction: An End-to-End Approach
Weiyi Gong, Tao Sun, Hexin Bai +3
Predicting electronic band structures from crystal structures is crucial for understanding structure-property correlations in materials science. First-principles approaches are acc…
Crystal Hypergraph Convolutional Networks
Alexander J. Heilman, Weiyi Gong, Qimin Yan
Graph representations of solid state materials that encode only interatomic distance lack geometrical resolution, resulting in degenerate representations that may map distinct stru…