Two-Step Machine Learning Approach for Charge-Transfer Coupling with Structurally Diverse Data-Reference-Cited by-同舟云学术

Two-Step Machine Learning Approach for Charge-Transfer Coupling with Structurally Diverse Data

Published:2023-12-29 Issue:1 Volume:128 Page:271-280
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Lin Hung-Hsuan¹²,Wang Chun-I¹³,Yang Chou-Hsun¹,Secario Muhammad Khari¹⁴⁵^ORCID,Hsu Chao-Ping¹⁶^ORCID

Affiliation:

1. Institute of Chemistry, Academia Sinica, 128 Section 2 Academia Road, Nankang, Taipei 115, Taiwan

2. Molecular Science and Digital Innovation Center, Genetics Generation Advancement Corp, No. 28, Ln. 36, Xinhu First Rd., Neihu, Taipei 114, Taiwan

3. Department of Chemistry, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801, United States

4. Taiwan International Graduate Program on Sustainable Chemical Science & Technology, Academia Sinica Institute of Chemistry, 128 Academia Road Sec.2, Nankang, Taipei 115, Taiwan

5. Department of Applied Chemistry, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan

6. Division of Physics, National Center for Theoretical Sciences, 1, Section 4, Roosevelt Road, Taipei 106, Taiwan

Funder

National Science and Technology Council

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.3c04524

Reference74 articles.

1. Organic semiconductor crystals

2. Charge Transport in Organic Semiconductors: The Perspective from Nonadiabatic Molecular Dynamics

3. Theoretical Study of the Organic Photovoltaic Electron Acceptor PCBM: Morphology, Electronic Structure, and Charge Localization

4. A map of high-mobility molecular semiconductors

5. Charge transport in high mobility molecular semiconductors: classical models and new theories