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Title: Electronic spectra from TDDFT and machine learning in chemical space

Abstract

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. In this work, 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-singlet vertical electronic spectra of over 20 000 synthetically feasible small organic molecules with up to eight CONF atoms. The prediction errors decay monotonously as a function of training set size. For a training set of 10 000 molecules, CC2 excitation energies can be reproduced to within ±0.1 eV for the remaining molecules. Analysis of our spectral database via chromophore counting suggests that even higher accuracies can be achieved. Based on the evidence collected, we discuss open challenges associated with data-driven modeling of high-lying spectra and transition intensities.

Authors:
 [1];  [2];  [2];  [3]
+ Show Author Affiliations
  1. Univ. of Basel (Switzerland)
  2. California State Univ. (CalState), Long Beach, CA (United States)
  3. Univ. of Basel (Switzerland); Argonne National Lab. (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility (ALCF)
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC); Swiss National Science Foundation (SNF)
OSTI Identifier:
1392463
Alternate Identifier(s):
OSTI ID: 1229636
Grant/Contract Number:  
AC02-06CH11357; PP00P2_138932
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 143; Journal Issue: 8; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Ramakrishnan, Raghunathan, Hartmann, Mia, Tapavicza, Enrico, and von Lilienfeld, O. Anatole. Electronic spectra from TDDFT and machine learning in chemical space. United States: N. p., 2015. Web. doi:10.1063/1.4928757.
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Ramakrishnan, Raghunathan, Hartmann, Mia, Tapavicza, Enrico, & von Lilienfeld, O. Anatole. Electronic spectra from TDDFT and machine learning in chemical space. United States. https://doi.org/10.1063/1.4928757
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Ramakrishnan, Raghunathan, Hartmann, Mia, Tapavicza, Enrico, and von Lilienfeld, O. Anatole. Tue . "Electronic spectra from TDDFT and machine learning in chemical space". United States. https://doi.org/10.1063/1.4928757. https://www.osti.gov/servlets/purl/1392463.
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@article{osti_1392463,
title = {Electronic spectra from TDDFT and machine learning in chemical space},
author = {Ramakrishnan, Raghunathan and Hartmann, Mia and Tapavicza, Enrico and von Lilienfeld, O. Anatole},
abstractNote = {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. In this work, 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-singlet vertical electronic spectra of over 20 000 synthetically feasible small organic molecules with up to eight CONF atoms. The prediction errors decay monotonously as a function of training set size. For a training set of 10 000 molecules, CC2 excitation energies can be reproduced to within ±0.1 eV for the remaining molecules. Analysis of our spectral database via chromophore counting suggests that even higher accuracies can be achieved. Based on the evidence collected, we discuss open challenges associated with data-driven modeling of high-lying spectra and transition intensities.},
doi = {10.1063/1.4928757},
journal = {Journal of Chemical Physics},
number = 8,
volume = 143,
place = {United States},
year = {Tue Aug 25 00:00:00 EDT 2015},
month = {Tue Aug 25 00:00:00 EDT 2015}
}
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https://doi.org/10.1063/1.4928757
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