ArticleChemical science2023
Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-phase systems.
Article in Chemical science, 2023. 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.
- Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- A Review of Ammonia (NH3) Adsorption in Porous Solids.Chemical reviews · 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
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.Digital discovery · 2025Article
- MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.Journal of the American Chemical Society · 2025Article
- One Size Fits All? Development of the CPOSS209 Data Set of Experimental and Hypothetical Polymorphs for Testing Computational Modeling Methods.Crystal growth & design · 2025Article
- Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution.Journal of the American Chemical Society · 2025Article
- Incorporating Neural Networks into the AMOEBA Polarizable Force Field.The journal of physical chemistry. B · 2024Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electronic structure methods offer in principle accurate predictions of molecular properties, however, their applicability is limited by computational costs. Empirical methods are cheaper, but come with inherent approximations and are dependent on the quality and quantity of training data. The rise of machine learning (ML) force fields (FFs) exacerbates limitations related to training data even further, especially for condensed-phase systems for which the generation of large and high-quality training datasets is difficult. Here, we propose a hybrid ML/classical FF model that is parametrized exclusively on high-quality
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