Literature DB >> 20370113

Quantum grow--a quantum dynamics sampling approach for growing potential energy surfaces and nonadiabatic couplings.

Oded Godsi1, Michael A Collins, Uri Peskin.   

Abstract

A quantum sampling algorithm for the interpolation of diabatic potential energy matrices by the Grow method is introduced. The new procedure benefits from penetration of the wave packet into classically forbidden regions, and the accurate quantum mechanical description of nonadiabatic transitions. The increased complexity associated with running quantum dynamics is reduced by using approximate low order expansions of the nuclear wave function within a Multi-configuration time-dependent Hartree scheme during the Grow process. The sampling algorithm is formulated and applied for three representative test cases, demonstrating the recovery of analytic potentials by the interpolated ones, and the convergence of a dynamic observable.

Year:  2010        PMID: 20370113     DOI: 10.1063/1.3364817

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  2 in total

1.  Machine Learning for Electronically Excited States of Molecules.

Authors:  Julia Westermayr; Philipp Marquetand
Journal:  Chem Rev       Date:  2020-11-19       Impact factor: 60.622

2.  Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface.

Authors:  Shichen Lin; Daoling Peng; Weitao Yang; Feng Long Gu; Zhenggang Lan
Journal:  J Chem Phys       Date:  2021-12-07       Impact factor: 3.488

  2 in total

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