No abstract

Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential for understanding the natural processes and design of highly-efficient photovoltaic devices. LHCs are open systems, where quantum effects may play a crucial role for almost perfect utilization of solar energy. Simulation of energy transfer with inclusion of quantum effects can be done within the framework of dissipative quantum dynamics (QD), which are computationally expensive. Thus, artificial intelligence (AI) offers itself as a tool for reducing the computational cost. Here we suggest AI-QD approach using AI to directly predict QD as a function of time and other parameters such as temperature, reorganization energy, etc., completely circumventing the need of recursive step-wise dynamics propagation in contrast to the traditional QD and alternative, recursive AI-based QD approaches. Our trajectory-learning AI-QD approach is able to predict the correct asymptotic behavior of QD at infinite time. We demonstrate AI-QD on seven-sites Fenna–Matthews–Olson (FMO) complex.

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-shot trajectory learning approach that allows us to directly make an ultrafast prediction of the entire trajectory of the reduced density matrix for a new set of such simulation parameters as temperature and reorganization energy. The whole 10-ps-long propagation takes 70 ms as we demonstrate on the comparatively large quantum system, the Fenna–Matthews–Olsen (FMO) complex. Our approach also significantly reduces time and memory requirements for training.

We demonstrate that artificial intelligence (AI) can learn four-dimensional (4D) atomistic systems in the spacetime continuum. Given the initial conditions – nuclear positions and velocities at time zero – the proposed 4D-atomistic AI (4D-A2I) models can predict nuclear positions at any time in the future or past for the simplest systems as we show for H2. For larger polyatomic molecules, AI is capable of learning distant but finite future as we demonstrate for an ethanol molecule. 4D-A2I models provide direct access to a multitude of properties at a given time such as geometries, velocities, forces, and energies which can be used in simulating physicochemical transformations and spectra. Our approach can be used as a cost-efficient alternative to traditional molecular dynamics. We show an example of a 4D-A2I model describing the dynamical behavior of ethanol at the coupled-cluster level with the speed of one nanosecond simulation time per one hour wall-clock time on a single GPU card – a previously unachievable feat with traditional Born–Oppenheimer molecular dynamics. 4D-A2I model is also demonstrated to provide direct access to atomistic time-resolved details of physicochemical transformations.

Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential for understanding the natural processes and design of highly-efficient photovoltaic devices. LHCs are open systems, where quantum effects may play a crucial role for almost perfect utilization of solar energy. Simulation of energy transfer with inclusion of quantum effects can be done within the framework of dissipative quantum dynamics (QD), which are computationally expensive. Thus, artificial intelligence (AI) offers itself as a tool for reducing the computational cost. We suggest AI-QD approach using AI to directly predict QD as a function of time and other parameters such as temperature, reorganization energy, etc., completely circumventing the need of recursive step-wise dynamics propagation in contrast to the traditional QD and alternative, recursive AI-based QD approaches. Our trajectory-learning AI-QD approach is able to predict the correct asymptotic behavior of QD at infinite time. We demonstrate AI-QD on seven-sites Fenna–Matthews–Olson (FMO) complex.