Adaptive quantum/molecular mechanics: what have we learned, where are we, and where do we go from here?: Adaptive QM/MM

Duster, Adam W.; Wang, Chun-Hung; Garza, Christina M.; Miller, Danielle E.; Lin, Hai

doi:10.1002/wcms.1310

Adaptive quantum/molecular mechanics: what have we learned, where are we, and where do we go from here?: Adaptive QM/MM

Journal Article · Mon Apr 17 00:00:00 EDT 2017 · Wiley Interdisciplinary Reviews: Computational Molecular Science

DOI:https://doi.org/10.1002/wcms.1310· OSTI ID:1461758

Duster, Adam W. ^[1]; Wang, Chun-Hung ^[1]; Garza, Christina M. ^[1]; Miller, Danielle E. ^[1]; Lin, Hai ^[1]

Univ. of Colorado, Denver, CO (United States)

Adaptive quantum-mechanics/molecular-mechanics (QM/MM) methods feature on-the-fly reclassification of atoms as QM or MM during a molecular dynamics(MD) simulation, allowing the location and contents of the QM subsystem to be dynamically updated as needed. Such flexibility is a distinct advantage over conventional QM/MM, where a ‘static’ boundary is retained between the QM and MM subsystems. The ‘dynamic’ boundary in adaptive QM/MM allows a finite-size QM to sustain simulations with an arbitrary length of time. Here, to ensure smooth transitions between QM and MM, the energy or forces are interpolated.Special treatments are applied so that artifacts are eliminated or minimized.Recent developments have shed light on the relationship between the adaptive algorithms that describe Hamiltonian and non-Hamiltonian systems. Originally developed to model an ion solvated in bulk solvent, adaptive QM/MM has been enhanced in many aspects, including the treatment of molecular fragments in macromolecules, monitoring molecules entering/leaving binding sites, and tracking proton transfer via the Grotthuss mechanism. Because the size of the QM region can be set as small as possible in adaptive QM/MM, the computational costs can be kept low. Small QM subsystems also facilitate the utilization of high-level QM theory and long simulation time, which can potentially lead to new insights.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory, Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC).

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1461758

Journal Information:: Wiley Interdisciplinary Reviews: Computational Molecular Science, Journal Name: Wiley Interdisciplinary Reviews: Computational Molecular Science Journal Issue: 5 Vol. 7; ISSN 1759-0876

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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