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@ASK-Berkeley

ASK-Berkeley

Research group at UC Berkeley, working on machine learning methods for the physical sciences.

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  1. EScAIP EScAIP Public

    [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential

    Python 52 5

  2. Neural-Spectral-Methods Neural-Spectral-Methods Public

    [ICLR 2024] Neural Spectral Methods: Self-supervised learning in the spectral domain.

    Python 46 3

  3. physics-NNs-hard-constraints physics-NNs-hard-constraints Public

    [ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.

    Python 34 4

  4. MLFF-distill MLFF-distill Public

    Forked from ishanthewizard/MLFF-distill

    [ICLR 2025] Official Implementation of "Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians"

    Jupyter Notebook 19

  5. OM-TPS OM-TPS Public

    [ICML 2025] Repurposing pre-trained score-based generative models for transition path sampling by minimizing the Onsager-Machlup (OM) action

    Jupyter Notebook 18

  6. StABlE-Training StABlE-Training Public

    [TMLR 2025] Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

    Python 14 1

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