ArticleThe Journal of chemical physics2017
Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations.
Article in The Journal of chemical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Accelerating Quantum Mechanical/Molecular Mechanical (QM/MM) Simulations with Stochastic Iso-Kinetic Nosé-Hoover Multiple Time Step Integrator in CHARMM.The journal of physical chemistry. B · 2026Article
- Predictive free energy simulations through hierarchical distillation of quantum Hamiltonians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications.The journal of physical chemistry. B · 2025Article
- Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.Journal of chemical theory and computation · 2025Article
- Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.Nature communications · 2024Article
- Training machine learning potentials for reactive systems: A Colab tutorial on basic models.Journal of computational chemistry · 2024Article
- Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates.Chemical science · 2024Article
- Article
- Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation.The Journal of chemical physics · 2023Article
- FacilitatingPhysical chemistry chemical physics : PCCP · 2022Article
- BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations.The journal of physical chemistry letters · 2022Article
- AffordableThe journal of physical chemistry. A · 2021Article
- Doubly Polarized QM/MM with Machine Learning Chaperone Polarizability.Journal of chemical theory and computation · 2021Article
- Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution.Journal of chemical theory and computation · 2021Article
- Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.Journal of chemical theory and computation · 2021Article
- Reaction Path-Force Matching in Collective Variables: Determining Ab Initio QM/MM Free Energy Profiles by Fitting Mean Force.Journal of chemical theory and computation · 2021Article
- Accelerated computation of free energy profile at ab initio quantum mechanical/molecular mechanical accuracy via a semi-empirical reference potential. II. Recalibrating semi-empirical parameters with force matching.Physical chemistry chemical physics : PCCP · 2019Article
- Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.Journal of chemical theory and computation · 2019Article
- Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.Journal of chemical theory and computation · 2018Article
- The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.Chemical science · 2018Article
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
Abstract
Ab initio quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulation is a useful tool to calculate thermodynamic properties such as potential of mean force for chemical reactions but intensely time consuming. In this paper, we developed a new method using the internal force correction for low-level semiempirical QM/MM molecular dynamics samplings with a predefined reaction coordinate. As a correction term, the internal force was predicted with a machine learning scheme, which provides a sophisticated force field, and added to the atomic forces on the reaction coordinate related atoms at each integration step. We applied this method to two reactions in aqueous solution and reproduced potentials of mean force at the ab initio QM/MM level. The saving in computational cost is about 2 orders of magnitude. The present work reveals great potentials for machine learning in QM/MM simulations to study complex chemical processes.
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.