Accelerating wavepacket propagation with machine learning

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Kanishka Singh
  • Ka Hei Lee
  • Daniel Peláez
  • Annika Bande

Externe Organisationen

  • Helmholtz-Zentrum Berlin für Materialien und Energie GmbH
  • Freie Universität Berlin (FU Berlin)
  • Universität Paris-Süd
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)2360-2373
Seitenumfang14
FachzeitschriftJournal of computational chemistry
Jahrgang45
Ausgabenummer28
PublikationsstatusVeröffentlicht - 2 Sept. 2024

Abstract

In this work, we discuss the use of a recently introduced machine learning (ML) technique known as Fourier neural operators (FNO) as an efficient alternative to the traditional solution of the time-dependent Schrödinger equation (TDSE). FNOs are ML models which are employed in the approximated solution of partial differential equations. For a wavepacket propagating in an anharmonic potential and for a tunneling system, we show that the FNO approach can accurately and faithfully model wavepacket propagation via the density. Additionally, we demonstrate that FNOs can be a suitable replacement for traditional TDSE solvers in cases where the results of the quantum dynamical simulation are required repeatedly such as in the case of parameter optimization problems (e.g., control). The speed-up from the FNO method allows for its combination with the Markov-chain Monte Carlo approach in applications that involve solving inverse problems such as optimal and coherent laser control of the outcome of dynamical processes.

ASJC Scopus Sachgebiete

Zitieren

Accelerating wavepacket propagation with machine learning. / Singh, Kanishka; Lee, Ka Hei; Peláez, Daniel et al.
in: Journal of computational chemistry, Jahrgang 45, Nr. 28, 02.09.2024, S. 2360-2373.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Singh, K, Lee, KH, Peláez, D & Bande, A 2024, 'Accelerating wavepacket propagation with machine learning', Journal of computational chemistry, Jg. 45, Nr. 28, S. 2360-2373. https://doi.org/10.1002/jcc.27443
Singh K, Lee KH, Peláez D, Bande A. Accelerating wavepacket propagation with machine learning. Journal of computational chemistry. 2024 Sep 2;45(28):2360-2373. doi: 10.1002/jcc.27443
Singh, Kanishka ; Lee, Ka Hei ; Peláez, Daniel et al. / Accelerating wavepacket propagation with machine learning. in: Journal of computational chemistry. 2024 ; Jahrgang 45, Nr. 28. S. 2360-2373.
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AU - Lee, Ka Hei

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AU - Bande, Annika

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