# Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning

@article{Fu2022SimulateTC, title={Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning}, author={Xiang Fu and Tian Xie and Nathan J. Rebello and Bradley D. Olsen and T. Jaakkola}, journal={ArXiv}, year={2022}, volume={abs/2204.10348} }

Molecular dynamics (MD) simulation is the workhorse of various scientiﬁc domains but is limited by high computational cost. Learning-based force ﬁelds have made major progress in accelerating ab-initio MD simulation but are still not fast enough for many real-world applications that require long-time MD simulation. In this paper, we adopt a diﬀerent machine learning approach where we coarse-grain a physical system using graph clustering, and model the system evolution with a very large time…

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