Machine learning the frontier orbital energies of SubPc based triads-Reference-Cited by-同舟云学术

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Machine learning the frontier orbital energies of SubPc based triads

Published:2022-09-13 Issue:10 Volume:28 Page:
ISSN:1610-2940
Container-title:Journal of Molecular Modeling
language:en
Short-container-title:J Mol Model

Author:

Storm Freja E.,Folkmann Linnea M.,Hansen Thorsten,Mikkelsen Kurt V.^ORCID

Funder

Kemisk Institut, Københavns Universitet

Lundbeckfonden

Danmarks Frie Forskningsfond

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Catalysis

Link

https://link.springer.com/content/pdf/10.1007/s00894-022-05262-0.pdf

Reference67 articles.

1. Cheng P, Li G, Zhan X, Yang Y (2018) Next-generation organic photovoltaics based on non-fullerene acceptors. Nat Photonics 12:131–142

2. Inganäs O (2018) Organic photovoltaics over three decades. Adv Mater 30:1800388

3. Brabec CJ, Gowrisanker S, Halls JJM, Laird D, Jia S, Williams SP (2010) Polymer-fullerene bulk-heterojunction solar cells. Adv Mater 22:3839–3856

4. Tang CW (1986) Two-layer organic photovoltaic cell. Appl Phys Lett 48:183–185

5. Liang Y, Xu Z, Xia J, Tsai S-T, Wu Y, Li G, Ray C, Yu L (2010) For the bright future-bulk heterojunction polymer solar cells with power conversion efficiency of 7.4%. Adv Mater 22:E135–E138

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3. Synthesis, characterization and band gap energy of new water soluble fluorescent diethanolamine-boron-subphthalocyanine dye using B nanoparticles and SiB6 microparticles;Journal of Photochemistry and Photobiology A: Chemistry;2023-03

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