Hydrogen bond network structures of protonated 2,2,2-trifluoroethanol/ethanol mixed clusters probed by infrared spectroscopy combined with a deep-learning structure sampling approach: the origin of the linear type network preference in protonated fluoroalcohol clusters

Po-Jen Hsu, Atsuya Mizuide, Jer-Lai Kuo and Asuka Fujii

Phys. Chem. Chem. Phys., 2024, 26, 27751
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Application of modern artificial intelligence techniques in the development of organic molecular force fields

Junmin Chen, Qian Gao, Miaofei Huang and Kuang Yu

Phys. Chem. Chem. Phys., 2025, 27, 2294
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Advancing density functional tight-binding method for large organic molecules through equivariant neural networks

Leonardo Medrano Sandonas, Mirela Puleva, Zekiye Erarslan, Ricardo Parra Payano, Martin Stöhr, Gianaurelio Cuniberti and Alexandre Tkatchenko

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Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials

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Digital Discovery, 2025, 4, 1478
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Hamiltonian Grid-Based QM/MM Method with Mean-Field Embedding for Simulating Arbitrary Slab Geometries

Hiroshi Nakano and Hisao Nakamura

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Graph neural networks classify molecular geometry and design novel order parameters of crystal and liquid

Satoki Ishiai, Katsuhiro Endo and Kenji Yasuoka

The Journal of Chemical Physics, 2023, 159
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Efficient and accurate spatial mixing of machine learned interatomic potentials for materials science

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A deep position-encoding model for predicting olfactory perception from molecular structures and electrostatics

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High-performance QM/MM Enhanced Sampling Molecular Dynamics Simulations with GENESIS SPDYN and QSimulate-QM

Kiyoshi Yagi, Klaas Gunst, Toru Shiozaki and Yuji Sugita

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Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications

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Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions

Abdul Raafik Arattu Thodika, Xiaoliang Pan, Yihan Shao and Kwangho Nam

J. Chem. Theory Comput., 2025, 21, 817
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Training dependency of neural network interatomic potential for molecular dynamics simulation of Ru-Si-O mixed system

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How exascale computing can shape drug design: A perspective from multiscale QM/MM molecular dynamics simulations and machine learning-aided enhanced sampling algorithms

Giulia Rossetti and Davide Mandelli

Current Opinion in Structural Biology, 2024, 86, 102814
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Learning QM/MM potential using equivariant multiscale model

Yao-Kun Lei, Kiyoshi Yagi and Yuji Sugita

The Journal of Chemical Physics, 2024, 160
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Efficient Free Energies from a Simplified Electrostatic Embedding QM/MM Approach Based on Electrostatic Potential Fitted Operators

Simone Bonfrate, Nicolas Ferré and Miquel Huix-Rotllant

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Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians

Chenghan Li and Garnet Kin-Lic Chan

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Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

Amir Omranpour, Pablo Montero De Hijes, Jörg Behler and Christoph Dellago

The Journal of Chemical Physics, 2024, 160
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IceCoder: Identification of Ice Phases in Molecular Simulation Using Variational Autoencoder

Dibyendu Maity and Suman Chakrabarty

J. Chem. Theory Comput., 2025, 21, 1916
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Novel approach for designing order parameters of clathrate hydrate structures by graph neural network

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Advances in the Simulations of Enzyme Reactivity in the Dawn of the Artificial Intelligence Age

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Efficient Training of Neural Network Potentials for Chemical and Enzymatic Reactions by Continual Learning

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DP/MM: A Hybrid Model for Zinc–Protein Interactions in Molecular Dynamics

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Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution

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Prediction of Charged Small Molecule Conformations in Solution Using a Balanced ML/MM Potential

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

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Chemical Physics Reviews, 2025, 6
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Multiscale Force Field Model Based on a Graph Neural Network for Complex Chemical Systems

Zhaoxin Xie, Yanheng Li, Yijie Xia, Jun Zhang, Sihao Yuan, Cheng Fan, Yi Isaac Yang and Yi Qin Gao

J. Chem. Theory Comput., 2025, 21, 2501
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Advancing Multiscale Molecular Modeling with Machine Learning-Derived Electrostatics

Jonathan A. Semelak, Ignacio Pickering, Kate Huddleston, Justo Olmos, Juan Santiago Grassano, Camila Mara Clemente, Salvador I. Drusin, Marcelo Marti, Mariano Camilo Gonzalez Lebrero, Adrian E. Roitberg and Dario A. Estrin

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Multiscale Machine Learning Prediction of Infrared Spectra of Solvated Molecules

Patrizia Mazzeo, Lorenzo Cupellini and Benedetta Mennucci

J. Chem. Theory Comput., 2026, 22, 1883
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Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules

Paul Katzberger, Felix Pultar and Sereina Riniker

J. Chem. Theory Comput., 2025, 21, 7450
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Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies

Moritz Bensberg, Marco Eckhoff, F. Emil Thomasen, William Bro-Jørgensen, Matthew S. Teynor, Valentina Sora, Thomas Weymuth, Raphael T. Husistein, Frederik E. Knudsen, Anders Krogh, Kresten Lindorff-Larsen, Markus Reiher and Gemma C. Solomon

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TorchANI-Amber: Bridging Neural Network Potentials and Classical Biomolecular Simulations

Ignacio Pickering, Jonathan A. Semelak, Jinze Xue and Adrian E. Roitberg

J. Phys. Chem. B, 2025, 129, 11927
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