Literature DB >> 35653199

Accelerating Ab Initio Quantum Mechanical and Molecular Mechanical (QM/MM) Molecular Dynamics Simulations with Multiple Time Step Integration and a Recalibrated Semiempirical QM/MM Hamiltonian.

Xiaoliang Pan1, Richard Van1, Evgeny Epifanovsky2, Jian Liu3, Jingzhi Pu4, Kwangho Nam5, Yihan Shao1.   

Abstract

Molecular dynamics (MD) simulations employing ab initio quantum mechanical and molecular mechanical (ai-QM/MM) potentials are considered to be the state of the art, but the high computational cost associated with the ai-QM calculations remains a theoretical challenge for their routine application. Here, we present a modified protocol of the multiple time step (MTS) method for accelerating ai-QM/MM MD simulations of condensed-phase reactions. Within a previous MTS protocol [Nam J. Chem. Theory Comput. 2014, 10, 4175], reference forces are evaluated using a low-level (semiempirical QM/MM) Hamiltonian and employed at inner time steps to propagate the nuclear motions. Correction forces, which arise from the force differences between high-level (ai-QM/MM) and low-level Hamiltonians, are applied at outer time steps, where the MTS algorithm allows the time-reversible integration of the correction forces. To increase the outer step size, which is bound by the highest-frequency component in the correction forces, the semiempirical QM Hamiltonian is recalibrated in this work to minimize the magnitude of the correction forces. The remaining high-frequency modes, which are mainly bond stretches involving hydrogen atoms, are then removed from the correction forces. When combined with a Langevin or SIN(R) thermostat, the modified MTS-QM/MM scheme remains robust with an up to 8 (with Langevin) or 10 fs (with SIN(R)) outer time step (with 1 fs inner time steps) for the chorismate mutase system. This leads to an over 5-fold speedup over standard ai-QM/MM simulations, without sacrificing the accuracy in the predicted free energy profile of the reaction.

Entities:  

Year:  2022        PMID: 35653199     DOI: 10.1021/acs.jpcb.2c02262

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   3.466


  1 in total

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

Authors:  Xiaoliang Pan; Junjie Yang; Richard Van; Evgeny Epifanovsky; Junming Ho; Jing Huang; Jingzhi Pu; Ye Mei; Kwangho Nam; Yihan Shao
Journal:  J Chem Theory Comput       Date:  2021-09-01       Impact factor: 6.578

  1 in total

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