ArticleJournal of the American Chemical Society2025
Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution.
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Accurate and Time-Efficient Condensed-Phase Free Energy Simulations with Reaction Specific Δ-Machine Learning Potentials in CHARMM.Journal of chemical theory and computation · 2026Article
- Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.Journal of the American Chemical Society · 2026Article
- A Tutorial on Mechanistic Modeling of Nonstatistical Reactivity: Example of Light and Heat Effects in the Garratt-Braverman/[1,5]‑H Shift of Ene-diallenes.ACS physical chemistry Au · 2026Article
- Multiscale machine learning molecular mechanics for mechanism and stereoselectivity of Diels-Alderase catalysis.Nature communications · 2026Article
- Ab Initio Molecular Dynamics Simulations for Organic Chemists─It is About Time!Journal of the American Chemical Society · 2026Review
- Computing Solvation Free Energies of Small Molecules with Experimental Accuracy.Journal of the American Chemical Society · 2026Article
- Toward Improving Multiple Time Step QM/MM Simulations with Δ-Machine Learning.The journal of physical chemistry. B · 2025Article
- Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.Journal of chemical theory and computation · 2025Article
- Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules.Journal of chemical theory and computation · 2025Article
- Efficient Multistate Free-Energy Calculations with QM/MM Accuracy Using Replica-Exchange Enveloping Distribution Sampling.The journal of physical chemistry. B · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present the design and implementation of a novel neural network potential (NNP) and its combination with an electrostatic embedding scheme, commonly used within the context of hybrid quantum-mechanical/molecular-mechanical (QM/MM) simulations. Substitution of a computationally expensive QM Hamiltonian by an NNP with the same accuracy largely reduces the computational cost and enables efficient sampling in prospective MD simulations, the main limitation faced by traditional QM/MM setups. The model relies on the recently introduced anisotropic message passing (AMP) formalism to compute atomic interactions and encode symmetries found in QM systems. AMP is shown to be highly efficient in terms of both data and computational costs and can be readily scaled to sample systems involving more than 350 solute and 40,000 solvent atoms for hundreds of nanoseconds using umbrella sampling. Most deviations of AMP predictions from the underlying DFT ground truth lie within chemical accuracy (4.184 kJ mol
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.