In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics
(NAMD) in large realistic systems has received high research interest in recent years …

It has been recently shown that supervised machine learning (ML) algorithms can accurately
and efficiently predict long-time population dynamics of dissipative quantum systems given …

Abstract Machine learning has emerged as a promising paradigm to study the quantum
dissipative dynamics of open quantum systems. To facilitate the use of our recently …

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 …

Nonadiabatic dynamics is key for understanding solar energy conversion and
photochemical processes in condensed phases. This often involves the non-Markovian …

The long short-term memory recurrent neural network (LSTM-RNN) approach is applied to
realize the trajectory-based nonadiabatic dynamics within the framework of the symmetrical …

Nonadiabatic quantum dynamics is important for understanding light-harvesting processes,
but its propagation with traditional methods can be rather expensive. Here we present a one …

We propose a new model class aimed at predicting dynamical trajectories from high-
dimensional empirical data. This is done by combining variational autoencoders and (spatio …

Stochastic processes have found numerous applications in science, as they are broadly
used to model a variety of natural phenomena. Due to their intrinsic randomness and …