Machine learning for exciton dynamics: Qy trajectories

In this folder we report the data of our paper, recently posted on the arXiv. In particular we provide the energy gap trajectories for each BChl of monomer A of the Fenna-Matthews-Olson (FMO) complex. These were computed using QM/MM and TDDFT, and then they were predicted using multi layer perceptrons with different methods to select the training data ( Random, Correlation, Frobenius and Taxicab).