A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection
Author:
Affiliation:
1. Key Laboratory of Cluster Science of Ministry of Education
2. Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials
3. School of Chemistry and Chemical Engineering
4. Beijing Institute of Technology
5. Beijing
Abstract
A time and money efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT based organic solar cells is reported. Green solvents are also selected using machine learning predicted Hansen solubility parameters.
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
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/2021/TA/D1TA04742F
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