Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th-based organic solar cells with over 15% efficiency-Reference-Cited by-同舟云学术

Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th-based organic solar cells with over 15% efficiency

Published:2022 Issue:8 Volume:10 Page:4170-4180
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Mahmood Asif¹^ORCID,Irfan Ahmad²,Wang Jin-Liang¹^ORCID

Affiliation:

1. Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 100081, China

2. Department of Chemistry, College of Science, King Khalid University, Abha 61413, Saudi Arabia

Abstract

A multi-stage machine learning and molecular dynamics simulation-assisted pipeline is introduced for the time- and cost-efficient design and screening of small molecule acceptors for organic solar cells.

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/2022/TA/D1TA09762H

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1. Green solvent-processed, high-performance organic solar cells achieved by outer side-chain selection of selenophene-incorporated Y-series acceptors

2. A bromine and chlorine concurrently functionalized end group for benzo[1,2-b:4,5-b′]diselenophene-based non-fluorinated acceptors: a new hybrid strategy to balance the crystallinity and miscibility of blend films for enabling highly efficient polymer solar cells