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Adaptive quantum/molecular mechanics: what have we learned, where are we, and where do we go from here?
Author(s) -
Duster Adam W.,
Wang ChunHung,
Garza Christina M.,
Miller Danielle E.,
Lin Hai
Publication year - 2017
Publication title -
wiley interdisciplinary reviews: computational molecular science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.126
H-Index - 81
eISSN - 1759-0884
pISSN - 1759-0876
DOI - 10.1002/wcms.1310
Subject(s) - qm/mm , molecular dynamics , hamiltonian (control theory) , statistical physics , physics , statistical mechanics , molecular mechanics , computer science , monte carlo method , quantum mechanics , mathematics , mathematical optimization , statistics
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. 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. WIREs Comput Mol Sci 2017, 7:e1310. doi: 10.1002/wcms.1310 This article is categorized under: Electronic Structure Theory > Combined QM/MM Methods Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods Software > Molecular Modeling

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