Electronic spectra from TDDFT and machine learning in chemical space.

@article{Ramakrishnan2015ElectronicSF,
  title={Electronic spectra from TDDFT and machine learning in chemical space.},
  author={Raghunathan Ramakrishnan and Mia Hartmann and Enrico Tapavicza and O. Anatole von Lilienfeld},
  journal={The Journal of chemical physics},
  year={2015},
  volume={143 8},
  pages={
          084111
        }
}
Due to its favorable computational efficiency, time-dependent (TD) density functional theory (DFT) enables the prediction of electronic spectra in a high-throughput manner across chemical space. Its predictions, however, can be quite inaccurate. We resolve this issue with machine learning models trained on deviations of reference second-order approximate coupled-cluster (CC2) singles and doubles spectra from TDDFT counterparts, or even from DFT gap. We applied this approach to low-lying singlet… 

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