  Obtaining the exciton dynamics of large photosynthetic complexes by using
mixed quantum mechanics/molecular mechanics (QM/MM) is computationally
demanding. We propose a machine learning technique, multi-layer perceptrons, as
a tool to reduce the time required to compute excited state energies. With this
approach we predict time-dependent density functional theory (TDDFT) excited
state energies of bacteriochlorophylls in the Fenna-Matthews-Olson (FMO)
complex. Additionally we compute spectral densities and exciton populations
from the predictions. Different methods to determine multi-layer perceptron
training sets are introduced, leading to several initial data selections. In
addition, we compute spectral densities and exciton populations. Once
multi-layer perceptrons are trained, predicting excited state energies was
found to be significantly faster than the corresponding QM/MM calculations. We
showed that multi-layer perceptrons can successfully reproduce the energies of
QM/MM calculations to a high degree of accuracy with prediction errors
contained within 0.01 eV (0.5%). Spectral densities and exciton dynamics are
also in agreement with the TDDFT results. The acceleration and accurate
prediction of dynamics strongly encourage the combination of machine learning
techniques with ab-initio methods.
