Issue 47, 2022

Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells

Abstract

Organic solar cells (OSCs) have witnessed significant improvement in power conversion efficiency (PCE) in the last decade. The structural flexibility of organic semiconductors provides vast search space for potential candidates of OSCs, but discovering new materials from search space with traditional approaches such as DFT is computationally expensive and time-consuming. Machine learning (ML) is extensively used in OSCs to accelerate productivity and materials discovery. ML is gaining more attention due to the availability of large datasets, improved algorithms, and exponentially growing computational power. In this review, current progress, opportunity, and challenges for ML in OSCs have been identified. Given the rapid advances in this field, impactful techniques that have been useful in extracting meaningful insights are discussed. Finally, we elaborate upon the bottlenecks of the ML workflow with respect to data size, model interpretability, and extrapolation.

Graphical abstract: Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells

Article information

Article type
Review Article
Submitted
05 Сер 2022
Accepted
19 Жов 2022
First published
19 Жов 2022

J. Mater. Chem. C, 2022,10, 17781-17811

Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells

P. Malhotra, K. Khandelwal, S. Biswas, F. Chen and G. D. Sharma, J. Mater. Chem. C, 2022, 10, 17781 DOI: 10.1039/D2TC03276G

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements