Analyzing Dynamical Disorder for Charge Transport in Organic Semiconductors via Machine Learning-Reference-Cited by-同舟云学术

Analyzing Dynamical Disorder for Charge Transport in Organic Semiconductors via Machine Learning

Published:2021-05-04 Issue:6 Volume:17 Page:3750-3759
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Reiser Patrick¹²^ORCID,Konrad Manuel¹,Fediai Artem¹^ORCID,Léon Salvador³^ORCID,Wenzel Wolfgang¹,Friederich Pascal¹²^ORCID

Affiliation:

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

2. Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT), Am Fasanengarten 5, Karlsruhe 76131, Germany

3. Department of Industrial Chemical Engineering and Environment, Universidad Politécnica de Madrid, C/ José Gutierrez Abascal, 2, Madrid 28006, Spain

Funder

European Commission

Deutsche Forschungsgemeinschaft

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.1c00191

Reference79 articles.

1. Organic light-emitting diode (OLED) technology: materials, devices and display technologies

2. Nonfullerene Acceptor Molecules for Bulk Heterojunction Organic Solar Cells

3. Organic Lasers: Recent Developments on Materials, Device Geometries, and Fabrication Techniques

4. Synthetic Chemistry for Ultrapure, Processable, and High-Mobility Organic Transistor Semiconductors

5. Solution coating of large-area organic semiconductor thin films with aligned single-crystalline domains

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