Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics

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.

Automatic Evolution of Machine-Learning-Based Quantum Dynamics with Uncertainty Analysis

“…After all dynamical evolutions were broken into many small pieces of time durations, a unified ML model (CNN by Herrera Rodríguez and KRR by Ullah) was built to simulate the long-term quantum evolution of other similar reduced models when their early-time dynamics are known. Ullah et al employed the CNN model in the simulation of the exciton dynamics in the light-harvesting complexes and confirmed the excellent performance of this artificial intelligence-based open quantum dynamics approach. Banchi et al proposed to employ RNNs with gated recurrent unit (GRU) to simulate the non-Markovian quantum processes, starting from various initial conditions.…”

A comparative study of different machine learning methods for dissipative quantum dynamics

“…Recently, CNNs is used to study the excitation energy transfer in Fenna-Matthews-Olson light-harvesting complex. 34,35 Wu et al 122 used a hybrid CNN/LSTM network to predict long-time semiclassical and mixed quantum-classical dynamics of the spin-boson model. Lin et al 123,130 trained a multi-layer LSTM model to simulate the long-time dynamics of spin-boson model and used bootstrap method to estimate the confidence interval.…”

“…Machine learning (ML) offers an alternative route to accurate, yet greatly accelerated quantum dynamics calculations with minimum input information required. [32][33][34][35] In ML, predicting the future time-evolution of quantum mechanical observables based on past values can be formulated as time series forecasting problem. A time series is a set of data points recorded in consecutive time intervals.…”

“…Recently, many ML models have been applied to simulate the dynamics of quantum systems. [32][33][34][35][117][118][119][120][121][122][123][124][125][126] We note that ML can also be applied to quantum dynamics in a different context-namely as surrogate models for quantum chemical properties such as potential energies and forces in different electronic states as well as cou-plings between states eliminating the need for expensive (excited-state) electronic structure calculations. [127][128][129] Here we apply ML to propagate a quantum system assuming potential energies are readily available.…”

One-Shot Trajectory Learning of Open Quantum Systems Dynamics

“…12−23 ML-based approaches adopted in the literature so far can be divided into two categories: recursive 15−18 and nonrecursive. 19,20 ML-based recursive approaches where dynamics at some time t depends on the dynamics of previous time steps are quite successful, but there are some downsides for them. First, iterative propagation is inherently slow and may lead to error accumulation, and second, they need a short-time trajectory generated with traditional quantum dynamics methods to initiate trajectory propagation.…”