Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review

IF 6 3区 工程技术 Q2 ENERGY & FUELS
Solar RRL Pub Date : 2024-10-09 DOI:10.1002/solr.202400567
Yang Jiang, Chuang Yao, Yezi Yang, Jinshan Wang
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引用次数: 0

Abstract

Organic solar cells (OSCs), renowned for their lightweight, cost efficiency, and adaptability nature, stand out as a promising option for developing renewable energy. Improving the power conversion efficiency (PCE) of OSCs is essential, and researchers are delving into novel materials to achieve this. Traditional approaches are often laborious and costly, highlighting the need for predictive modeling. Machine learning (ML), especially via quantitative structure–property relationship (QSPR) models, is streamlining material development, with a goal to exceed a 20% PCE. In this review, the application of ML in OSCs is explored, and recent studies utilizing ML approaches for PCE prediction are reviewed, encompassing empirical functions, ML algorithms, self-devised ML frameworks, and the combination with automated experimental technologies. First, the benefits of ML in predicting PCE for OSCs are addressed. Second, the development of high-efficiency predictive models for both fullerene and nonfullerene acceptors is delved into. The impact of various ML algorithm models on PCE prediction is then assessed, taking into account the construction of predictive models. Moreover, the quality of databases and the selection of descriptors are considered. Databases and descriptors based on experimental studies are further categorized. Finally, prospects for the future development of OSCs are proposed.

Abstract Image

预测有机太阳能电池功率转换效率的机器学习方法综述
有机太阳能电池(OSCs)以其轻便、低成本和适应性而闻名,是发展可再生能源的一个有前途的选择。提高OSCs的功率转换效率(PCE)至关重要,研究人员正在研究新的材料来实现这一目标。传统的方法通常是费力和昂贵的,突出了对预测建模的需求。机器学习(ML),特别是通过定量结构-性质关系(QSPR)模型,正在简化材料开发,目标是超过20%的PCE。本文探讨了机器学习在OSCs中的应用,并回顾了最近利用机器学习方法进行PCE预测的研究,包括经验函数、机器学习算法、自我设计的机器学习框架以及与自动化实验技术的结合。首先,讨论了机器学习在预测osc PCE方面的好处。其次,研究了富勒烯和非富勒烯受体的高效预测模型的发展。然后,考虑到预测模型的构建,评估各种ML算法模型对PCE预测的影响。此外,还考虑了数据库的质量和描述符的选择。基于实验研究的数据库和描述符进一步分类。最后,对OSCs的未来发展进行了展望。
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来源期刊
Solar RRL
Solar RRL Physics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
12.10
自引率
6.30%
发文量
460
期刊介绍: Solar RRL, formerly known as Rapid Research Letters, has evolved to embrace a broader and more encompassing format. We publish Research Articles and Reviews covering all facets of solar energy conversion. This includes, but is not limited to, photovoltaics and solar cells (both established and emerging systems), as well as the development, characterization, and optimization of materials and devices. Additionally, we cover topics such as photovoltaic modules and systems, their installation and deployment, photocatalysis, solar fuels, photothermal and photoelectrochemical solar energy conversion, energy distribution, grid issues, and other relevant aspects. Join us in exploring the latest advancements in solar energy conversion research.
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