ArticleNature communications2025
Machine learning of charges and long-range interactions from energies and forces.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Systematic trends in water properties across Jacob's ladder density functionals.Digital discovery · 2026Article
- Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential.The journal of physical chemistry letters · 2026Article
- Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Learning potential energy surfaces of hydrogen atom transfer reactions in peptides.Digital discovery · 2026Article
- Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models.Angewandte Chemie (International ed. in English) · 2026Review
- CHARMM-GUI Hybrid ML/MM Builder for Hybrid Machine Learning and Molecular Mechanical Modeling and Simulations.Journal of chemical information and modeling · 2026Article
- Foundation models for atomistic simulation of chemistry and materials.Nature reviews. Chemistry · 2026Review
- Extending the range of graph neural networks with global encodings.Nature communications · 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
- A foundation machine learning potential with polarizable long-range interactions for materials modelling.Nature communications · 2025Article
- Machine learning of charges and long-range interactions from energies and forces.Nature communications · 2025Article
- Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes.Journal of chemical theory and computation · 2025Article
- Learning non-local molecular interactions via equivariant local representations and charge equilibration.npj computational materials · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of material and chemical systems. However, standard machine learning interatomic potentials (MLIPs) often rely on short-range approximations, limiting their applicability to systems with significant electrostatics and dispersion forces. We recently introduced the Latent Ewald Summation (LES) method, which captures long-range electrostatics without explicitly learning atomic charges or charge equilibration. We benchmark LES on diverse and challenging systems, including charged molecules, ionic liquids, electrolyte solutions, polar dipeptides, surface adsorption, electrolyte/solid interfaces, and solid-solid interfaces. Here we show that LES can reproduce the exact atomic charges for classical systems with fixed charges and can infer dipole and quadrupole moments, as well as the dipole derivative with respect to atomic positions, for quantum mechanical systems. Moreover, LES can achieve better accuracy in energy and force predictions compared to methods that explicitly learn from charges.
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Registered trials
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.