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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
• Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning
  2023 · 5 citations
• Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks
  2019 · 4 citations
• Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
  2025 · 2 citations
• Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
  2025 · 1 citations
• Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
  2025 · 1 citations
• Predictive Scale-Bridging Simulations through Active Learning
  2022 · 1 citations
• Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials
  2023 · 1 citations
• Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
  2025
• GPU-Accelerated Charge-Equilibration for Shadow Molecular Dynamics in Python
  2025
• Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks
  2022