Stochastic Hessian-vector product matching augments force matching to instill second-order curvature into coarse-grained molecular dynamics potentials, outperforming force matching on slow-mode metrics for 8 of 9 test proteins.
Projected hessian learning: Fast curvature supervision for accurate machine- learning interatomic potentials.arXiv preprint arXiv:2603.04523, 2026
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mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.
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Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics
Stochastic Hessian-vector product matching augments force matching to instill second-order curvature into coarse-grained molecular dynamics potentials, outperforming force matching on slow-mode metrics for 8 of 9 test proteins.
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Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
mlip v2 is a new software release that integrates API redesign, e3j backend, eSEN model, improved charge modeling, and expanded simulation capabilities to support larger-scale molecular modeling.