ArticleScience advances2024
Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 54 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed.
- Using structure to evaluate the accuracy and transferability of machine learning potentials of biomolecules.RSC advances · 2026Article
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Bridging the accuracy-speed divide in reactive molecular dynamics with QuantaMind MD.Science advances · 2026Article
- Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Modeling dual-range atomic interactions with physicochemical principles for molecular force fields.Bioinformatics (Oxford, England) · 2026Article
- Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.Journal of chemical theory and computation · 2026Article
- Lithium Surface Restructuring and Dendrite-Like Protrusion Formation in Li-S Electrolytes From Reactive Molecular Simulations With Machine-Learned Potentials.Journal of computational chemistry · 2026Article
- Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.Journal of the American Chemical Society · 2026Article
- How Long Is Long Enough? Extrapolation of Machine-Learning Interatomic Potentials for Oligomeric and Polymeric Systems.Journal of chemical theory and computation · 2026Article
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.JACS Au · 2026Article
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.Quantitative biology (Beijing, China) · 2026Article
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Article
- aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials.Journal of chemical information and modeling · 2026Article
- Accurate density functional theory for noncovalent interactions in charged systems.Science advances · 2026Article
- Beyond Classical Force Fields: Physics-Driven Assessment of the Grappa Machine-Learned Force Field on the FoldBind Dataset.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.
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.