ArticleChemical reviews2021
Machine Learning Force Fields.
Article in Chemical reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 405 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.
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Who cites it
405 citing papers in PubMed.
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- Reasoning-Driven Design of Single-Atom Catalysts via a Multiagent Large Language Model Framework.ACS central science · 2026Article
- Improving Reliability of Machine Learning Interatomic Potentials with Physics-Informed Pretraining.Journal of chemical information and modeling · 2026Article
- Bridging the accuracy-speed divide in reactive molecular dynamics with QuantaMind MD.Science advances · 2026Article
- Improving coupled cluster theory for strongly correlated molecules with Kohn-Sham density encoding.Nature communications · 2026Article
- Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models.The journal of physical chemistry letters · 2026Article
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Data Compression of the D1200 Suite: Achieving the Optimal Balance Between Size and Accuracy with the Diet-D200 Subset.Journal of computational chemistry · 2026Article
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Editorial Board Members' Collection Series: Theory and Simulation of Nanostructures.Nanomaterials (Basel, Switzerland) · 2026Article
- Synergistic Zundel-Eigen mechanism for proton transport in protic ionic liquids unraveled by machine learning force fields and NMR spectra.National science review · 2026Article
- Article
- Atomistic machine learning with irreducible Cartesian natural tensors.Nature communications · 2026Article
- Design Rules for Tuning Thermal Lifetimes of Arylazopyrazole Photoswitches.ACS central science · 2026Article
- A Review of Ammonia (NH3) Adsorption in Porous Solids.Chemical reviews · 2026Article
- Automating Model Building for SPM Images of Biomolecules Using MISO.Journal of chemical information and modeling · 2026Article
- Machine Learning-Enhanced Molecular Dynamics of 4,4'-Bipyridine in a Break Junction: A Supramolecular Origin of the High Conductance.ACS nanoscience Au · 2026Article
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
345 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of
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.