Quantum dynamics evolution predicted by the long short-term memory network in the photosystem II reaction center

Abstract

Predicting future physical behavior from limited theoretical simulation data is an emerging research paradigm driven by the integration of artificial intelligence and quantum physics. In this work, charge transport (CT) behavior was predicted over extended time scales using a deep learning model-the long short-term memory (LSTM) network with an error-threshold training method-in the photosystem II reaction center (PSII-RC). Theoretical simulation data within 8 fs were used to train the modified LSTM network, yielding distinct predictions with differences on the order of $10^{-4}$ over prolonged periods compared to the training set collection time. The results highlight the potential of LSTM to uncover the underlying physics governing CT beyond conventional quantum physical methods. These findings warrant further investigation to fully explore the scope and efficacy of LSTM in advancing our understanding of photosynthesis at the molecular scale.

7 pages, 13 figures

References in corpus (12)

• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Measure for the Degree of Non-Markovian Behavior of Quantum Processes in Open Systems
• Deep learning for molecular design - a review of the state of the art
• An Experimental Review on Deep Learning Architectures for Time Series Forecasting
• Markovian and non-Markovian dynamics in quantum and classical systems
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Path integral approach to quantum thermodynamics
• Reduced density matrix for nonequilibrium steady states: A modified Redfield solution approach
• Non-Markovian decoherence dynamics of the hybrid quantum system with a cavity strongly coupling to a spin ensemble: a master equation approach
• Validity of Born-Markov master equations for single and two-qubit systems
• Nonequilibrium model of short-range repression in gene transcription regulation
• Non-Markovian to Markovian decay in structured environments with correlated disorder