Machine learning for the prediction of molecular dipole moments obtained by density functional theory
10.1186/s13321-018-0296-5
Crossref journal-article
Springer Science and Business Media LLC
Journal of Cheminformatics (297)
Bibliography

Pereira, F., & Aires-de-Sousa, J. (2018). Machine learning for the prediction of molecular dipole moments obtained by density functional theory. Journal of Cheminformatics, 10(1).

Authors 2
  1. Florbela Pereira (first)
  2. João Aires-de-Sousa (additional)
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Dates
Type When
Created 7 years ago (Aug. 22, 2018, 2:21 a.m.)
Deposited 5 years, 11 months ago (Sept. 20, 2019, 6:41 a.m.)
Indexed 4 weeks, 1 day ago (Aug. 3, 2025, 6:59 p.m.)
Issued 7 years ago (Aug. 22, 2018)
Published 7 years ago (Aug. 22, 2018)
Published Online 7 years ago (Aug. 22, 2018)
Published Print 6 years, 9 months ago (Dec. 1, 2018)
Funders 1
  1. Fundação para a Ciência e a Tecnologia 10.13039/501100001871

    Region: Europe

    gov (National government)

    Labels4
    1. Foundation for Science and Technology
    2. Portuguese Science and Technology Foundation
    3. Fundacao para a Ciencia e a Tecnologia
    4. FCT
    Awards2
    1. SFRH/BPD/108237/2015
    2. PEst-OE/QUI/UI0612/2013

@article{Pereira_2018, title={Machine learning for the prediction of molecular dipole moments obtained by density functional theory}, volume={10}, ISSN={1758-2946}, url={http://dx.doi.org/10.1186/s13321-018-0296-5}, DOI={10.1186/s13321-018-0296-5}, number={1}, journal={Journal of Cheminformatics}, publisher={Springer Science and Business Media LLC}, author={Pereira, Florbela and Aires-de-Sousa, João}, year={2018}, month=aug }