有机太阳能电池
有机分子
富勒烯
小分子
人工智能
随机森林
计算机科学
机器学习
多样性(控制论)
分子
光伏系统
纳米技术
化学
材料科学
工程类
有机化学
电气工程
生物化学
作者
Muhammad Ramzan Saeed Ashraf Janjua,Ahmad Irfan,Mohamed Hussien,Muhammad Ali,Muhammad Saqib,Muhammad Sulaman
标识
DOI:10.1002/ente.202200019
摘要
In recent years, development in organic solar cells speeds up and performance continuously increases. From the last few years, machine learning gains fame among scientists who are researching on organic solar cells. Herein, machine learning is used to screen the small‐molecule donors for organic solar cells. Molecular descriptors are used as input to train machine models. A variety of machine‐learning models are tested to find the suitable one. Random forest model shows best predictive capability (Pearson's coefficient = 0.93). New small‐molecule donors are also designed from easily synthesizable building units. Their power conversion efficiencies (PCEs) are predicted. Potential candidates with PCE > 11% are selected. The approach presented herein helps to select the efficient materials in short time with ease.
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