ArticleJournal of chemical theory and computation2021
Reaction Path-Force Matching in Collective Variables: Determining Ab Initio QM/MM Free Energy Profiles by Fitting Mean Force.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development.ACS omega · 2024Review
- Free Energy Profile Decomposition Analysis for QM/MM Simulations of Enzymatic Reactions.Journal of chemical theory and computation · 2023Article
- 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
- Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.RSC advances · 2023Article
- Multireference Generalization of the Weighted Thermodynamic Perturbation Method.The journal of physical chemistry. A · 2022Article
- FacilitatingPhysical chemistry chemical physics : PCCP · 2022Article
- AcceleratingThe journal of physical chemistry. B · 2022Article
- 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
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Authors and funding
9 authors.
Funding
Abstract
First-principles determination of free energy profiles for condensed-phase chemical reactions is hampered by the daunting costs associated with configurational sampling on ab initio quantum mechanical/molecular mechanical (AI/MM) potential energy surfaces. Here, we report a new method that enables efficient AI/MM free energy simulations through mean force fitting. In this method, a free energy path in collective variables (CVs) is first determined on an efficient reactive aiding potential. Based on the configurations sampled along the free energy path, correcting forces to reproduce the AI/MM forces on the CVs are determined through force matching. The AI/MM free energy profile is then predicted from simulations on the aiding potential in conjunction with the correcting forces. Such cycles of correction-prediction are repeated until convergence is established. As the instantaneous forces on the CVs sampled in equilibrium ensembles along the free energy path are fitted, this procedure faithfully restores the target free energy profile by reproducing the free energy mean forces. Due to its close connection with the reaction path-force matching (RP-FM) framework recently introduced by us, we designate the new method as RP-FM in collective variables (RP-FM-CV). We demonstrate the effectiveness of this method on a type-II solution-phase SN
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