Designing promising molecules for organic solar cells via machine learning assisted virtual screening-Reference-Cited by-同舟云学术

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Designing promising molecules for organic solar cells via machine learning assisted virtual screening

Published:2019 Issue:29 Volume:7 Page:17480-17488
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Sahu Harikrishna¹²³⁴⁵,Yang Feng¹²³⁴⁵,Ye Xiaobo¹²³⁴⁵,Ma Jing¹²³⁴⁵^ORCID,Fang Weihai¹²³⁴⁵,Ma Haibo¹²³⁴⁵^ORCID

Affiliation:

1. Key Laboratory of Mesoscopic Chemistry of MOE

2. School of Chemistry and Chemical Engineering

3. Nanjing University

4. Nanjing 210023

5. China

Abstract

Rational design of new OPV molecules via virtual screening of candidate materials using high-performing machine learning models.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2019/TA/C9TA04097H

Reference70 articles.

1. Organic and solution-processed tandem solar cells with 17.3% efficiency

2. Next-generation organic photovoltaics based on non-fullerene acceptors

3. Fine-tuning of the chemical structure of photoactive materials for highly efficient organic photovoltaics

4. Lifetime of Organic Salt Photovoltaics

5. Ternary solar cells with a mixed face-on and edge-on orientation enable an unprecedented efficiency of 12.1%

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