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Accelerating wavepacket propagation with machine learning
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DOI：10.1002/jcc.27443.png)
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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 Schr & ouml;dinger 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. The solution of the Time-Dependent Schr & ouml;dinger equation provides an accurate description of time-dependent molecular phenomena. The conventional numerical methods employed for this task often have high computational cost. In this work, we use a specific type of Machine Learning algorithm and show how our model can predict a complete wavepacket simulation in a single computation. This suggests a useful and efficient alternative to traditional methods, especially in applications that require a large number of repetitive dynamical calculations. image
Keyword:
Fourier neural operators
machine learning
quantum dynamics
期刊
IF:
4.8
论文数:
7.1K
被引数:
6.1W
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