Jul 2026· Journal of Chemical Theory and Computation· Vol 22, pp. 7544 - 7557· 0 citations· 120 references
Medicine
TL;DR
This work proposes 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), which involves constructing a simplified representation of a larger fine-grained system with the goal of significantly accelerating computations while maintaining the accuracy of the FG system when projected onto the CG variable distributions.
Abstract
Neural network potentials (NNPs), or neural network-based force fields, are gaining widespread attention for their ability to model complex chemical, materials, and biophysical systems. 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.
This work releases OpenGEM26 (Open Generated Ensemble of Molecules, 2026), a large-scale dataset comprising 200,000 unique molecules and 4.4 million conformations composed of H, C, N, O, S and Cl with up to ten heavy atoms, providing a high-quality resource and robust ML potential for efficient simulations of sulfur- and chlorine-containing organic molecules.
This work introduces implicit machine learning force fields, which replace explicit stacks of neural network layers with self-consistent fixed-point equations, and demonstrates this across three major classes of graph neural networks: invariant, equivariant Cartesian tensor, and SO(3)-equivariant spherical-tensor architectures.
J. Maeß, Leon Werner, J. Frank et al.· arXiv.org· 0 citations
The buffer region embedding strategy (BuRNN) is extended for hybrid machine-learned interaction potentials/molecular mechanics (MLIP/MM) simulations in complex environments and is established as a practical approach to perform MLIP/MM simulations in biomolecular systems.
M. Caspary, Radek Crha, Edgar Galicia-Andrés et al.· Journal of Chemical Informat...· 0 citations
This work lays the foundation for NNPs where solvation is an integral part of the model, enabling the development of multiscale NNPs for simulating large biomolecular systems.
Moritz Thürlemann, Felix Pultar, Igor Gordiy et al.· Scientific Data· 0 citations
This work proposes an operator-centric framework in which the external (nuclear) potential, expressed in an AO basis, serves as the model input and builds hierarchical, body-ordered representations of atomic configurations that closely mirror the principles underlying several popular atom-centered descriptors.
Jigyasa Nigam, T. Smidt, G. Dusson· Journal of Chemical Physics· 2 citations
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 framework for incorporating molecular geometry and directionality into coarse-grained machine-learned potentials through two complementary approaches: anisotropic density-based descriptors (AniSOAP) and symmetry-adapted equivariant message-passing neural networks (MACE-CG). Using Gay-Berne particles and coarse-grained representations of benzene, formamide, and water, we demonstrate that explicitly retaining molecular anisotropy substantially improves the prediction of energies, forces, and torques relative to isotropic representations. AniSOAP provides an effective linear baseline when molecular shape is well approximated by ellipsoidal symmetry, while symmetry-adapted MACE-CG enables the incorporation of arbitrary molecular point-group symmetries. For water, whose orientational degrees of freedom are poorly represented by ellipsoidal descriptors alone, symmetry-adapted rigid-body features improve energy, force, and torque prediction by resolving orientational degeneracies inherent to isotropic and moment-of-inertia-based representations. These results show that information loss in coarse-grained modeling is governed not only by mapping resolution but also by the symmetry and geometric information retained in the representation, providing a systematic route toward more expressive and transferable coarse-grained machine-learned potentials.
Arthur Y. Lin, Tejas Dahiya, Rose K. Cersonsky· 0 citations
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