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Construction of coarse-grained molecular dynamics with many-body non-Markovian memory
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We introduce a machine-learning-based coarse-grained molecular dynamics (CGMD) model that faithfully retains the many-body nature of the inter-molecular dissipative interactions. Unlike common empirical CG models, the present model is constructed based on the Mori-Zwanzig formalism and naturally inherits the heterogeneous state-dependent memory term rather than matching the mean-field metrics such as the velocity auto-correlation function. Numerical results show that preserving the many-body nature of the memory term is crucial for predicting the collective transport and diffusion processes, where empirical forms generally show limitations.
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Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion
Using the Atomic Cluster Expansion fitted to atomistic forces, the authors construct coarse-grained models whose radial and angular distributions converge to the atomistic reference as many-body body order increases.
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