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• Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
  2022 · 18 citations
• Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
  2022 · 10 citations
• DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
  2023 · 8 citations
• Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials
  2022 · 5 citations
• Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
  2025 · 1 citations
• Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
  2024 · 1 citations
• Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
  2026
• Real-time interpretation of neutron vibrational spectra with symmetry-equivariant Hessian matrix prediction
  2025
• A deep learning approach to solve forward differential problems on graphs
  2022