40 citations · 50 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
Symmetry-guided data-driven discovery of native quantum defects in two-dimensional materials
Jeng-Yuan Tsai, Weiyi Gong, Qimin Yan
Drawing on their atomically thin structure, two-dimensional (2D) materials present a groundbreaking avenue for the precision fabrication and systematic manipulation of quantum defe…
Bridging deep learning force fields and electronic structures with a physics-informed approach
Yubo Qi, Weiyi Gong, Qimin Yan
This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional l…
Incorporation of density scaling constraint in density functional design via contrastive representation learning
Weiyi Gong, Tao Sun, Hexin Bai +7
In a data-driven paradigm, machine learning (ML) is the central component for developing accurate and universal exchange-correlation (XC) functionals in density functional theory (…
Structure motif centric learning framework for inorganic crystalline systems
Huta R. Banjade, Sandro Hauri, Shanshan Zhang +4
Incorporation of physical principles in a network-based machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for mat…
Intrinsic long range antiferromagnetic coupling in dilutely V doped CuInTe
Weiyi Gong, Ching-Him Leung, Chuen-Keung Sin +4
Despite the various magnetic orders mediated by superexchange mechanism, the existence of a long range antiferromagnetic (AFM) coupling is unknown. Based on DFT calculations, we di…