Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors-Reference-Cited by-同舟云学术

Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors

Published:2023 Issue:41 Volume:11 Page:22248-22258
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Suthar Rakesh¹^ORCID,T Abhijith¹^ORCID,Karak Supravat¹^ORCID

Affiliation:

1. Organic & Hybrid Electronic Device Laboratory, Department of Energy Science and Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India

Abstract

The machine learning approach was employed to explore the relationship between molecular structure and photovoltaic properties using frontier molecular orbital and RDKit descriptors, which enabled us to screen and identify potential donor and acceptor combinations for efficient organic solar cells.

Funder

Science and Engineering Research Board

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/2023/TA/D3TA04603F

Reference75 articles.

1. 25th Anniversary Article: Bulk Heterojunction Solar Cells: Understanding the Mechanism of Operation

2. Recent Advances in Bulk Heterojunction Polymer Solar Cells

3. Synergistic Plasmonic Responses of Multi-Shaped Au Nanostructures Hybridized with Few-Layer WS₂ Nanosheets for Organic Solar Cells

4. New Conjugated Polymers Based on Dithieno[2,3‐e:3′,2′‐g]Isoindole‐7,9(8H)‐Dione Derivatives for Applications in Nonfullerene Polymer Solar Cells

5. Double Asymmetric Core Optimizes Crystal Packing to Enable Selenophene‐based Acceptor with Over 18 % Efficiency in Binary Organic Solar Cells

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