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A Generalized Approach for Incorporating Geometry and Directionality into Coarse-Grained Machine-Learned Potentials
Machine-learned interatomic potentials have enabled highly accurate atomistic simulations, but extending these capabilities to coarse-grained systems remains challenging due to the loss of geometric and orientational information during coarse-graining. In this work, we present a generalized framewor
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