ReviewJournal of chemical theory and computation2020
Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.
Review in Journal of chemical theory and computation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 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
52 citing papers in PubMed.
- Building RNA coarse-grained force fields: Design principles and training strategies.Biophysical journal · 2026Review
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Enhanced Sampling and Free Energy Calculations in Protein Simulations.Advances in experimental medicine and biology · 2026Review
- Reactive Active Learning: An Efficient Approach for Training Machine Learning Interatomic Potentials for Reacting Systems.Journal of chemical theory and computation · 2025Article
- Biomolecular Condensates as Emerging Biomaterials: Functional Mechanisms and Advances in Computational and Experimental Approaches.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Modeling Brownian Motion as a Timelapse of the Physical, Persistent Trajectory.The journal of physical chemistry. B · 2025Article
- Toward Predictive Coarse-Grained Simulations of Biomolecular Condensates.Biochemistry · 2025Review
- Chromatin structures from integrated AI and polymer physics model.PLoS computational biology · 2025Article
- Integrative spatiotemporal modeling of biomolecular processes: Application to the assembly of the nuclear pore complex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Interpolating numerically exact many-body wave functions for accelerated molecular dynamics.Nature communications · 2025Article
- CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation.Journal of chemical information and modeling · 2025Article
- Generating Protein Structures for Pathway Discovery Using Deep Learning.Journal of chemical theory and computation · 2024Article
- Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex.bioRxiv : the preprint server for biology · 2024Article
- A Stochastic Landscape Approach for Protein Folding State Classification.Journal of chemical theory and computation · 2024Article
- Sodium Triflate Water-in-Salt Electrolytes in Advanced Battery Applications: A First-Principles-Based Molecular Dynamics Study.ACS applied materials & interfaces · 2024Article
- From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials.Journal of chemical information and modeling · 2024Article
- Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media.Journal of chemical theory and computation · 2024Article
- A Machine Learning Force Field for Bio-Macromolecular Modeling Based on Quantum Chemistry-Calculated Interaction Energy Datasets.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
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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.