ArticleNature communications2022
Self-consistent determination of long-range electrostatics in neural network potentials.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Integrating Coupled-Cluster Theory Within AI Workflows for Accurate Molecular Geometries.Journal of computational chemistry · 2026Article
- A unified machine-learning framework for ab initio multiscale modeling of liquids.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- pyEF: A Python Framework for QM and QM/MM Atom-Wise Electric Field Analysis.Journal of chemical theory and computation · 2026Article
- Generalization of long-range machine learning potentials in complex chemical spaces.Digital discovery · 2026Article
- qNEP: A Highly Efficient Neuroevolution Potential with Dynamic Charges for Large-Scale Atomistic Simulations.Journal of chemical theory and computation · 2026Article
- Long-range electrostatics for machine learning interatomic potentials is easier than we thought.The Journal of chemical physics · 2026Article
- A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials.Journal of chemical theory and computation · 2025Article
- Entropy Governs the Structure and Reactivity of Water Dissociation Under Electric Fields.Journal of the American Chemical Society · 2025Article
- Bulk Phase Dominates Sulfur Dioxide Hydrolysis over Interfacial Processes.Nature communications · 2025Article
- Computational Modeling of Reticular Materials: The Past, the Present, and the Future.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Electronic Paddlewheels Impact the Dynamics of Superionic Conduction in AgI.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine learning of charges and long-range interactions from energies and forces.Nature communications · 2025Article
- Carbon Dioxide Electroreduction on Gold without Metal or Organic Cations.ACS catalysis · 2025Article
- Global universal scaling and ultrasmall parameterization in machine-learning interatomic potentials with superlinearity.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications.The journal of physical chemistry. B · 2025Article
- Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes.Journal of chemical theory and computation · 2025Article
- PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems.Journal of chemical theory and computation · 2025Article
- Learning non-local molecular interactions via equivariant local representations and charge equilibration.npj computational materials · 2025Article
- On machine learnability of local contributions to interatomic potentials from density functional theory calculations.Scientific reports · 2024Article
Corrections and comments
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-based calculations, but with a fraction of the cost, enabling simulations of large systems over long timescales. However, implicit in the construction of neural network potentials is an assumption of locality, wherein atomic arrangements on the nanometer-scale are used to learn interatomic interactions. Because of this assumption, the resulting neural network models cannot describe long-range interactions that play critical roles in dielectric screening and chemical reactivity. Here, we address this issue by introducing the self-consistent field neural network - a general approach for learning the long-range response of molecular systems in neural network potentials that relies on a physically meaningful separation of the interatomic interactions - and demonstrate its utility by modeling liquid water with and without applied fields.
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