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In NNPs, the total energy of the system can be decomposed into atom-centered components, where the energies and forces are described using a deep neural network. However, despite the flexibility and accuracy of NNPs, they are typically more computationally intensive than classical molecular dynamics. There have been recent advances in embedding NNPs into a molecular mechanics (MM)-based environment to maximize efficiency while retaining the accuracy of NNPs. In this work, we propose a method called NNP\/CG-MM, in which an all-atom NN force field is systematically embedded into a coarse-grained molecular mechanics environment (CG-MM). Coarse-graining (CG) involves constructing a simplified representation of a larger fine-grained (FG) system with the goal of significantly accelerating computations while maintaining the accuracy of the FG system when projected onto the CG variable distributions. The NNP-CG coupling terms are constructed using the multiscale CG force-matching (MS-CG) method. The scheme is tested on liquids and in capturing features of the hydrophobic effect, where three-body correlations in the CG solvent can play an important role.<\/jats:p>","DOI":"10.1021\/acs.jctc.6c00741","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T05:13:28Z","timestamp":1785129208000},"page":"7544-7557","source":"Crossref","is-referenced-by-count":2,"title":["NNP\/CG-MM: Embedding\nof All-Atom Neural Network Potentials\ninto a Coarse-Grained Molecular Mechanics Environment"],"prefix":"10.1021","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4935-3586","authenticated-orcid":true,"given":"Kuntal","family":"Ghosh","sequence":"first","affiliation":[{"name":"James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago , , , ,","place":["Chicago, Illinois, United States, 60637"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3267-6748","authenticated-orcid":true,"given":"Gregory A.","family":"Voth","sequence":"additional","affiliation":[{"name":"James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago , , , ,","place":["Chicago, Illinois, United States, 60637"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,25]]},"reference":[{"issue":"16","key":"2026081111110049500_cit1","doi-asserted-by":"publisher","first-page":"10142","DOI":"10.1021\/acs.chemrev.0c01111","article-title":"Machine Learning Force Fields","volume":"121","author":"Unke","year":"2021","journal-title":"Chem. 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