Literature DB >> 26588116

Acceleration of Ab Initio QM/MM Calculations under Periodic Boundary Conditions by Multiscale and Multiple Time Step Approaches.

Kwangho Nam1.   

Abstract

Development of multiscale ab initio quantum mechanical and molecular mechanical (AI-QM/MM) method for periodic boundary molecular dynamics (MD) simulations and their acceleration by multiple time step approach are described. The developed method achieves accuracy and efficiency by integrating the AI-QM/MM level of theory and the previously developed semiempirical (SE) QM/MM-Ewald sum method [J. Chem. Theory Comput. 2005, 1, 2] extended to the smooth particle-mesh Ewald (PME) summation method. In the developed methods, the total energy of the simulated system is evaluated at the SE-QM/MM-PME level of theory to include long-range QM/MM electrostatic interactions, which is then corrected on the fly using the AI-QM/MM level of theory within the real space cutoff. The resulting energy expression enables decomposition of total forces applied to each atom into forces determined at the low-level SE-QM/MM method and correction forces at the AI-QM/MM level, to integrate the system using the reversible reference system propagator algorithm. The resulting method achieves a substantial speed-up of the entire calculation by minimizing the number of time-consuming energy and gradient evaluations at the AI-QM/MM level. Test calculations show that the developed multiple time step AI-QM/MM method yields MD trajectories and potential of mean force profiles comparable to single time step QM/MM results. The developed method, together with message passing interface (MPI) parallelization, accelerates the present AI-QM/MM MD simulations about 30-fold relative to the speed of single-core AI-QM/MM simulations for the molecular systems tested in the present work, making the method less than one order slower than the SE-QM/MM methods under periodic boundary conditions.

Entities:  

Year:  2014        PMID: 26588116     DOI: 10.1021/ct5005643

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  11 in total

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2.  Ambient-Potential Composite Ewald Method for ab Initio Quantum Mechanical/Molecular Mechanical Molecular Dynamics Simulation.

Authors:  Timothy J Giese; Darrin M York
Journal:  J Chem Theory Comput       Date:  2016-05-26       Impact factor: 6.006

3.  Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.

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Journal:  J Chem Theory Comput       Date:  2021-09-01       Impact factor: 6.578

4.  QM/MM free energy simulations: recent progress and challenges.

Authors:  Xiya Lu; Dong Fang; Shingo Ito; Yuko Okamoto; Victor Ovchinnikov; Qiang Cui
Journal:  Mol Simul       Date:  2016-07-05       Impact factor: 2.178

5.  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.

Authors:  Xiaoliang Pan; Pengfei Li; Junming Ho; Jingzhi Pu; Ye Mei; Yihan Shao
Journal:  Phys Chem Chem Phys       Date:  2019-09-11       Impact factor: 3.676

6.  Treating electrostatics with Wolf summation in combined quantum mechanical and molecular mechanical simulations.

Authors:  Pedro Ojeda-May; Jingzhi Pu
Journal:  J Chem Phys       Date:  2015-11-07       Impact factor: 3.488

7.  A simplified charge projection scheme for long-range electrostatics in ab initio QM/MM calculations.

Authors:  Xiaoliang Pan; Kwangho Nam; Evgeny Epifanovsky; Andrew C Simmonett; Edina Rosta; Yihan Shao
Journal:  J Chem Phys       Date:  2021-01-14       Impact factor: 3.488

8.  Dynamic Connection between Enzymatic Catalysis and Collective Protein Motions.

Authors:  Pedro Ojeda-May; Ameeq Ui Mushtaq; Per Rogne; Apoorv Verma; Victor Ovchinnikov; Christin Grundström; Beata Dulko-Smith; Uwe H Sauer; Magnus Wolf-Watz; Kwangho Nam
Journal:  Biochemistry       Date:  2021-07-12       Impact factor: 3.321

9.  Reaction Path-Force Matching in Collective Variables: Determining Ab Initio QM/MM Free Energy Profiles by Fitting Mean Force.

Authors:  Bryant Kim; Ryan Snyder; Mulpuri Nagaraju; Yan Zhou; Pedro Ojeda-May; Seth Keeton; Mellisa Hege; Yihan Shao; Jingzhi Pu
Journal:  J Chem Theory Comput       Date:  2021-07-20       Impact factor: 6.578

10.  Doubly Polarized QM/MM with Machine Learning Chaperone Polarizability.

Authors:  Bryant Kim; Yihan Shao; Jingzhi Pu
Journal:  J Chem Theory Comput       Date:  2021-11-01       Impact factor: 6.578

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