Electronic spectra from TDDFT and machine learning in chemical space-Reference-Cited by-同舟云学术

Electronic spectra from TDDFT and machine learning in chemical space

Published:2015-08-25 Issue:8 Volume:143 Page:084111
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Ramakrishnan Raghunathan¹,Hartmann Mia²,Tapavicza Enrico²,von Lilienfeld O. Anatole¹³

Affiliation:

1. Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland

2. Department of Chemistry and Biochemistry, California State University, 1250 Bellflower Boulevard, Long Beach, California 90840, USA

3. Argonne Leadership Computing Facility, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois 60439, USA

Funder

Swiss National Science Foundation

U.S. Department of Energy (Department of Energy)

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/1.4928757

Reference65 articles.

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3. Photoelectrochemical cells

4. Improving the performance of doped π-conjugated polymers for use in organic light-emitting diodes

5. Highly Efficient and Photostable Photosensitizer Based on BODIPY Chromophore

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