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• Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation
  2023 · 8 citations
• Simple and efficient algorithms for training machine learning potentials to force data
  2020 · 7 citations
• Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks
  2019 · 4 citations
• Transferable Molecular Charge Assignment Using Deep Neural Networks
  2018 · 3 citations
• Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
  2025 · 2 citations
• Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
  2025 · 1 citations
• Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials
  2023 · 1 citations
• Projected Hessian Learning: Fast Curvature Supervision for Accurate Machine-Learning Interatomic Potentials
  2026
• Learning Thermal Response Forces: A Method for Extending the Thermodynamic Transferability of Coarse-Grained Models via Machine-Learning
  2026
• Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
  2025
• Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks
  2022