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Quantum dynamics using path integral coarse-graining.
Musil, Félix; Zaporozhets, Iryna; Noé, Frank; Clementi, Cecilia; Kapil, Venkat.
Afiliação
  • Musil F; Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany.
  • Zaporozhets I; Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany.
  • Noé F; Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany.
  • Clementi C; Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany.
  • Kapil V; Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.
J Chem Phys ; 157(18): 181102, 2022 Nov 14.
Article em En | MEDLINE | ID: mdl-36379765
The vibrational spectra of condensed and gas-phase systems are influenced by thequantum-mechanical behavior of light nuclei. Full-dimensional simulations of approximate quantum dynamics are possible thanks to the imaginary time path-integral (PI) formulation of quantum statistical mechanics, albeit at a high computational cost which increases sharply with decreasing temperature. By leveraging advances in machine-learned coarse-graining, we develop a PI method with the reduced computational cost of a classical simulation. We also propose a simple temperature elevation scheme to significantly attenuate the artifacts of standard PI approaches as well as eliminate the unfavorable temperature scaling of the computational cost. We illustrate the approach, by calculating vibrational spectra using standard models of water molecules and bulk water, demonstrating significant computational savings and dramatically improved accuracy compared to more expensive reference approaches. Our simple, efficient, and accurate method has prospects for routine calculations of vibrational spectra for a wide range of molecular systems - with an explicit treatment of the quantum nature of nuclei.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article