Machine learning for exciton dynamics: Qy trajectories
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https://figshare.com/articles/dataset/Machine_learning_for_exciton_dynamics_Qy_trajectories/1611141/1
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资源简介：
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).
提供机构：
figshare
创建时间：
2016-01-20
