Accelerating wavepacket propagation with machine learning

authored by

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

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.

Organisation(s)

Institute of Inorganic Chemistry
PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines

External Organisation(s)

Helmholtz-Zentrum Berlin für Materialien und Energie (HZB)
Freie Universität Berlin (FU Berlin)
Universite Paris-Sud XI

Type

Article

Journal

Journal of computational chemistry

No. of pages

14

ISSN

0192-8651

Publication date

21.06.2024

Publication status

E-pub ahead of print

Peer reviewed

Yes

ASJC Scopus subject areas

Chemistry(all), Computational Mathematics

Electronic version(s)

https://doi.org/10.1002/jcc.27443 (Access: Closed)