The
recurrent neural network with the long short-term memory cell
(LSTM-NN) is employed to simulate the long-time dynamics of open quantum
systems. The bootstrap method is applied in the LSTM-NN construction
and prediction, which provides a Monte Carlo estimation of a forecasting
confidence interval. Within this approach, a large number of LSTM-NNs
are constructed by resampling time-series sequences that were obtained
from the early stage quantum evolution given by numerically exact
multilayer multiconfigurational time-dependent Hartree method. The
built LSTM-NN ensemble is used for the reliable propagation of the
long-time quantum dynamics, and the simulated result is highly consistent
with the exact evolution. The forecasting uncertainty that partially
reflects the reliability of the LSTM-NN prediction is also given.
This demonstrates the bootstrap-based LSTM-NN approach is a practical
and powerful tool to propagate the long-time quantum dynamics of open
systems with high accuracy and low computational cost.
An on-the-fly surface-hopping simulation protocol is developed for the evaluation of transient absorption (TA) pump−probe (PP) signals of molecular systems exhibiting internal conversion to the electronic ground state. We study the nonadiabatic dynamics of azomethane and the associating TA PP spectra at three levels of the electronic-structure theory, OM2/MRCI, SA-CASSCF, and XMS-CASPT2. The impact of these methods on the population dynamics and time-resolved TA PP signals is substantially different. This difference is attributed to the strong non-Condon effects that must be taken into account for the proper understanding and interpretation of time-resolved TA PP signals of nonadiabatic polyatomic systems. This shows that the combination of the dynamical and spectral simulations definitely provides more accurate and detailed information on the microscopic mechanisms of photophysical and photochemical processes. Hence the simulation of time-resolved spectroscopic signals provides another important dimension to examine the accuracy of quantum chemistry methods.
The
machine learning approaches are applied in the dynamical simulation
of open quantum systems. The long short-term memory recurrent neural
network (LSTM-RNN) models are used to simulate the long-time quantum
dynamics, which are built based on the key information of the short-time
evolution. We employ various hyperparameter optimization methods,
including simulated annealing, Bayesian optimization with tree-structured
parzen estimator, and random search, to achieve the automatic construction
and adjustment of the LSTM-RNN models. The implementation details
of three hyperparameter optimization methods are examined, and among
them, the simulated annealing approach is strongly recommended due
to its excellent performance. The uncertainties of the machine learning
models are comprehensively analyzed by the combination of bootstrap
sampling and Monte Carlo dropout approaches, which give the prediction
confidence of the LSTM-RNN models in the simulation of the open quantum
dynamics. This work builds an effective machine learning approach
to simulate the dynamics evolution of open quantum systems. In addition,
the current study provides an efficient protocol to build optimal
neural networks and estimate the trustiness of the machine learning
models.
We report that the photoinduced dynamics of the phytochrome chromophore is strongly dependent on the protonation/deprotonation states of the pyrrole ring. The on-the-fly surface hopping dynamics simulations were performed to study the photoisomerization of different protonation/deprotonation phytochrome chromophore models. The simulation results indicate that the deprotonations at the pyrrole rings significantly modify the photoinduced nonadiabatic dynamics, leading to distinctive population decay dynamics and different reaction channels. Such feature can be well explained by the formation of the different hydrogen bond network patterns. Therefore, the proper understanding of the photoisomerization mechanism of phytochrome chromophore must take the hydrogen bond network into account. This work provides the new insights into the photobiological functions of phytochrome chromophore and suggests the possible ideas to control of its photoconversion processes for further rational engineering in optical applications.
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