Machine Learning Applications in Organic Solar Cells

Today, the growing need for energy due to the development of human societies is inevitable. Given the limited energy resources of the planet as well as the environmental pollution caused by the consumption of fossil fuels, the use of solar energy as a clean energy is an undeniable necessity. The use...

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Bibliographic Details
Published in:	Baspārish (Tihrān. Online) Vol. 11; no. 4; pp. 51 - 64
Main Authors:	Ali Akbar Yousefi, Mohammad Ali M Nekouei, Amir Hossein Brendjchi, Maghsoud Amiri
Format:	Journal Article
Language:	Persian
Published:	Iran Polymer & Petrochemical Institute 01-02-2022
Subjects:	machine learning organic solar cell power conversion effciency supervised learning unsupervised learning
Online Access:	Get full text
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Summary:	Today, the growing need for energy due to the development of human societies is inevitable. Given the limited energy resources of the planet as well as the environmental pollution caused by the consumption of fossil fuels, the use of solar energy as a clean energy is an undeniable necessity. The use of photovoltaic converters, i.e. solar cells, is one of the efective solutions for the optimal use of this huge source of energy. So far, several sudies have been conducted on the design, manufacture and optimization of solar cells. One of the mos invaluable approaches is machine learning methods. Machine learning is considered as a new branch of academic science that provides valuable new information by processing exising data. Areas of application of machine learning in the design and manufacture of polymer solar cells are divided into three general categories: predicting the solar cells efciency, selecting appropriate materials, and optimizing the manufacturing process. In this article, we have tried to briefy introduce the machine learning methods and then explain their applications in the feld of designing and manufacturing solar cells. Due to several research activities, this article is limited to peer-reviewed articles in the years 2019 and 2020.
ISSN:	2252-0449 2538-5445
DOI:	10.22063/basparesh.2021.2836.1541