  Predicting future physical behavior through the limited theoretical
simulation data available is an emerging research paradigm resulted by the
integration of artificial intelligence technology and quantum physics. In this
work, the charge-transport(CT) behavior was forecasted over a long time by a
deep learning model, the long short-term memory (LSTM) network with error
threshold training method in the photosynthesis II reaction center (PSII-RC).
The theoretical simulation data within 8 fs was fed to the modified LSTM
network for training, which brings out a distinct prediction with difference of
$10^{-4}$ orders of magnitude over a long time period compared to the
collection time for training sets. The results indicate the potential of
employing LSTM to reveal the physics governing CT in addition to quantum
physical methods. The implications of this work warrant further investigation
to fully elucidate the scope and efficacy of LSTM for advancing our
understanding of photosynthesis at the molecular scale.
