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From QM/MM to ML/MM: A new era in multiscale modeling

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Grassano, Juan Santiago, Pickering, Ignacio, Roitberg, Adrian E., Estrin, Dario A., Semelak, Jonathan A.·2025·Chemical Physics Reviews

DOI 10.1063/5.0260078

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Abstract

Hybrid machine-learning/molecular-mechanics (ML/MM) methods extend the classical QM/MM paradigm by replacing the quantum description with neural network interatomic potentials trained to reproduce accurately quantum-mechanical (QM) results. By describing only the chemically active region with ML and the surrounding environment with molecular mechanics (MM), ML/MM models achieve near-QM/MM fidelity at a fraction of the computational cost, enabling routine simulation of reaction mechanisms, vibrational spectra, and binding free energies in complex biological or condensed-phase environments. The key challenge lies in coupling the ML and MM regions, a task addressed through three main strategies: (1) mechanical embedding (ME), where ML regions interact with fixed MM charges via classical electrostatics; (2) polarization-corrected mechanical embedding (PCME), where a vacuum-trained ML potential is supplemented post hoc with electrostatic corrections; and (3) environment-integrated embedding (EIE), where ML potentials are trained with explicit inclusion of MM-derived fields, enhancing accuracy but requiring specialized data. Since ML/MM builds on the scaffolding of QM/MM, most proposed coupling strategies rely heavily on electrostatics, polarization, and other physicochemical concepts, and the development and analysis of ML/MM schemes sits naturally at the intersection of physical chemistry and modern data science. This review surveys the conceptual foundations of ML/MM schemes, classifies existing implementations, and highlights key applications and open challenges, providing a critical snapshot of the current state-of-the-art and positioning ML/MM not merely as a computational alternative but as the natural evolution of QM/MM toward data-driven, scalable multiscale modeling.

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Authors: Grassano, J. S., Pickering, I., Roitberg, A. E., Estrin, D. A., & Semelak, J. A.
Affiliations: Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
University of Florida (US)
CONICET—Universidad de Buenos Aires, Instituto de Química-Física de los Materiales, Medio Ambiente y Energía (INQUIMAE), Ciudad Universitaria 2 , Pabellón 2, C1428EHA Buenos Aires,
Corresponding author: Semelak, Jonathan A.
ORCID: Grassano, Juan Santiago
Pickering, Ignacio
Roitberg, Adrian E.
Estrin, Dario A.
Semelak, Jonathan A.
Venue: Chemical Physics Reviews
Publisher: AIP Publishing
Published: 2025-11-10
Volume: 6
Issue: 4
ISSN: 2688-4070 (ISSN-L)
DOI: 10.1063/5.0260078
OpenAlex: W4416088803
Cited by: 20
References: 146
Citations by year: 2026: 19 · 2025: 1
FWCI: 4.22
Type: journal article
Language: en
Open access: diamond
doi.org
Locations: Chemical Physics Reviews (publishedVersion)
ChemRxiv (acceptedVersion, cc-by)
Funders: Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación (PICT 2020-01828)
Secretaría de Ciencia y Técnica, Universidad de Buenos Aires (UBACYT 20020220200002BA)
Consejo Nacional de Investigaciones Científicas y Técnicas (11220200102757CO)
National Science Foundation (Award OAC-2311632)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords: QM/MM, neural network interatomic potentials, multiscale modeling, electrostatic embedding, reaction mechanism, binding free energy, vibrational spectra

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From QM/MM to ML/MM: A new era in multiscale modeling

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