Artificial design of organic emitters <i>via</i> a genetic algorithm enhanced by a deep neural network-Reference-Cited by-同舟云学术

Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network

Published:2024 Issue:7 Volume:15 Page:2618-2639
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Nigam AkshatKumar¹²,Pollice Robert¹²^ORCID,Friederich Pascal¹²³⁴^ORCID,Aspuru-Guzik Alán¹²⁵⁶⁷

Affiliation:

1. Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada

2. Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada

3. Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

4. Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany

5. Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada

6. Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St., Ontario M5S 3E5, Canada

7. Department of Materials Science & Engineering, University of Toronto, 184 College St., Ontario M5S 3E4, Canada

Abstract

An artificial molecular design workflow using a genetic algorithm with molecular strings and artificial neural networks applied to organic molecules with inverted singlet-triplet gaps finds 1000 candidates with appreciable fluorescence rates.

Funder

Canada First Research Excellence Fund

Natural Resources Canada

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

University of Toronto

Canada Foundation for Innovation

Government of Ontario

Compute Canada

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D3SC05306G

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