ArticleJournal of chemical theory and computation2023
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions.
Article in Journal of chemical theory and computation, 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.
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Bridging quantum mechanics to liquid properties via a universal organic force field.Nature communications · 2026Article
- Generalization of long-range machine learning potentials in complex chemical spaces.Digital discovery · 2026Article
- DSKO: Dancing through DFTB Parametrization.Journal of chemical theory and computation · 2026Article
- Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives.ChemPlusChem · 2025Article
- Rapid Access to Small Molecule Conformational Ensembles in Organic Solvents Enabled by Graph Neural Network-Based Implicit Solvent Model.Journal of the American Chemical Society · 2025Article
- Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution.Journal of the American Chemical Society · 2025Article
- Learning non-local molecular interactions via equivariant local representations and charge equilibration.npj computational materials · 2025Article
- Machine-learned molecular mechanics force fields from large-scale quantum chemical data.Chemical science · 2024Article
- Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-phase systems.Chemical science · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Simulations of molecular systems using electronic structure methods are still not feasible for many systems of biological importance. As a result, empirical methods such as force fields (FF) have become an established tool for the simulation of large and complex molecular systems. The parametrization of FF is, however, time-consuming and has traditionally been based on experimental data. Recent years have therefore seen increasing efforts to automatize FF parametrization or to replace FF with machine-learning (ML) based potentials. Here, we propose an alternative strategy to parametrize FF, which makes use of ML and gradient-descent based optimization while retaining a functional form founded in physics. Using a predefined functional form is shown to enable interpretability, robustness, and efficient simulations of large systems over long time scales. To demonstrate the strength of the proposed method, a fixed-charge and a polarizable model are trained on
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.