Predicting quantum evolutions of excitation energy transfer in a light-harvesting complex using multi-optimized recurrent neural networks

Abstract

Constructing models to discover physics underlying magnanimous data is a traditional strategy in data mining which has been proved to be powerful and successful. In this work, a multi-optimized recurrent neural network (MRNN) is utilized to predict the dynamics of photosynthetic excitation energy transfer (EET) in a light-harvesting complex. The original data set produced by the master equation were trained to forecast the EET evolution. An agreement between our prediction and the theoretical deduction with an accuracy of over 99.26\% is found, showing the validity of the proposed MRNN. A time-segment polynomial fitting multiplied by a unit step function results in a loss rate of the order of $10^{-5}$, showing a striking consistence with analytical formulations for the photosynthetic EET. The work sets up a precedent for accurate EET prediction from large data set by establishing analytical descriptions for physics hidden behind, through minimizing the processing cost during the evolution of week-coupling EET.

16 pages, 9 figures

References in corpus (6)

• Perspective: Numerically "exact" approach to open quantum dynamics: The hierarchical equations of motion (HEOM)
• Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
• Machine learning based energy-free structure predictions of molecules (closed and open-shell), transition states, and solids
• Machine learning for excitation energy transfer dynamics
• Influence of the coupled-dipoles on photosynthetic performance in a photosynthetic quantum heat engine
• Charge-transport enhanced by the quantum entanglement in the Photosystem II reaction center