Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data-Reference-Cited by-同舟云学术

Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data

Published:2022-07-25 Issue:15 Volume:34 Page:6912-6920
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Miyake Yuta¹,Kranthiraja Kakaraparthi¹^ORCID,Ishiwari Fumitaka¹²^ORCID,Saeki Akinori¹²^ORCID

Affiliation:

1. Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan

2. Innovative Catalysis Science Division, Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI), Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan

Funder

Core Research for Evolutional Science and Technology

Japan Society for the Promotion of Science

Advanced Low Carbon Technology Research and Development Program

Iketani Science and Technology Foundation

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.2c01294

Reference54 articles.

1. Ternary Blend Organic Solar Cells: Understanding the Morphology from Recent Progress

2. Toward High-Efficiency Organic Photovoltaics: Perspectives on the Origin and Role of Energetic Disorder

3. New Phase for Organic Solar Cell Research: Emergence of Y-Series Electron Acceptors and Their Perspectives

4. An Electron Acceptor Challenging Fullerenes for Efficient Polymer Solar Cells

5. Fullerene-Free Polymer Solar Cells with over 11% Efficiency and Excellent Thermal Stability

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