Details
| Original language | English |
|---|---|
| Pages (from-to) | 2360-2373 |
| Number of pages | 14 |
| Journal | Journal of computational chemistry |
| Volume | 45 |
| Issue number | 28 |
| Publication status | Published - 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.
Keywords
- Fourier neural operators, machine learning, quantum dynamics
ASJC Scopus subject areas
- Chemistry(all)
- General Chemistry
- Mathematics(all)
- Computational Mathematics
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In: Journal of computational chemistry, Vol. 45, No. 28, 02.09.2024, p. 2360-2373.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Accelerating wavepacket propagation with machine learning
AU - Singh, Kanishka
AU - Lee, Ka Hei
AU - Peláez, Daniel
AU - Bande, Annika
N1 - Publisher Copyright: © 2024 The Author(s). Journal of Computational Chemistry published by Wiley Periodicals LLC.
PY - 2024/9/2
Y1 - 2024/9/2
N2 - 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.
AB - 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.
KW - Fourier neural operators
KW - machine learning
KW - quantum dynamics
UR - http://www.scopus.com/inward/record.url?scp=85196514834&partnerID=8YFLogxK
U2 - 10.1002/jcc.27443
DO - 10.1002/jcc.27443
M3 - Article
AN - SCOPUS:85196514834
VL - 45
SP - 2360
EP - 2373
JO - Journal of computational chemistry
JF - Journal of computational chemistry
SN - 0192-8651
IS - 28
ER -