ArticleThe journal of physical chemistry letters2023
Physics-Inspired Equivariant Descriptors of Nonbonded Interactions.
Article in The journal of physical chemistry letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Simultaneous learning of static and dynamic charges.Physical chemistry chemical physics : PCCP · 2026Article
- 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
- Machine learning of charges and long-range interactions from energies and forces.Nature communications · 2025Article
- Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales.Journal of chemical theory and computation · 2025Article
- Prediction rigidities for data-driven chemistry.Faraday discussions · 2025Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- A physics-aware neural network for protein-ligand interactions with quantum chemical accuracy.Chemical science · 2024Article
- Multiscale Modeling of Aqueous Electric Double Layers.Chemical reviews · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrostatic or dispersion interactions. We present an extension of the long distance equivariant (LODE) framework that can handle diverse LR interactions in a consistent way and seamlessly integrates with preexisting methods by building new sets of atom centered features. We provide a direct physical interpretation of these using the multipole expansion, which allows for simpler and more efficient implementations. The framework is applied to simple toy systems as proof of concept and a heterogeneous set of molecular dimers to push the method to its limits. By generalizing LODE to arbitrary asymptotic behaviors, we provide a coherent approach to treat arbitrary two- and many-body nonbonded interactions in the data-driven modeling of matter.
Identifiers
What OpenQuestion holds
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.