Learning from Fullerenes and Predicting for Y6: Machine Learning and High-Throughput Screening of Small Molecule Donors for Organic Solar Cells
Ahmad Irfan
Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha, 61413 Saudi Arabia
Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, Abha, 61413 Saudi Arabia
Search for more papers by this authorMohamed Hussien
Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, Abha, 61413 Saudi Arabia
Pesticide Formulation Department, Central Agricultural Pesticide Laboratory, Agricultural Research Center, Dokki, Giza, 12618 Egypt
Search for more papers by this authorMuhammad Yasir Mehboob
Department of Chemistry, University of Okara, Okara, 56300 Pakistan
Search for more papers by this authorAziz Ahmad
Interdisciplinary Research Center for Hydrogen and Energy Storage (IRC-HES), King Fahd University of Petroleum and Minerals, KFUPM, Box 5040, Dhahran, 31261 Saudi Arabia
Search for more papers by this authorCorresponding Author
Muhammad Ramzan Saeed Ashraf Janjua
Chemistry Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia
Search for more papers by this authorAhmad Irfan
Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha, 61413 Saudi Arabia
Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, Abha, 61413 Saudi Arabia
Search for more papers by this authorMohamed Hussien
Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, Abha, 61413 Saudi Arabia
Pesticide Formulation Department, Central Agricultural Pesticide Laboratory, Agricultural Research Center, Dokki, Giza, 12618 Egypt
Search for more papers by this authorMuhammad Yasir Mehboob
Department of Chemistry, University of Okara, Okara, 56300 Pakistan
Search for more papers by this authorAziz Ahmad
Interdisciplinary Research Center for Hydrogen and Energy Storage (IRC-HES), King Fahd University of Petroleum and Minerals, KFUPM, Box 5040, Dhahran, 31261 Saudi Arabia
Search for more papers by this authorCorresponding Author
Muhammad Ramzan Saeed Ashraf Janjua
Chemistry Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261 Saudi Arabia
Search for more papers by this authorAbstract
In recent years, research on the development of organic solar cells has increased significantly. For the last few years, machine learning (ML) has been gaining the attention of the scientific community working on organic solar cells. Herein, ML is used to screen small molecule donors for organic solar cells. ML models are fed by molecular descriptors. Various ML models are employed. The predictive capability of a support vector machine is found to be higher (Pearson's coefficient = 0.75). The best small donors with fullerene acceptors are selected to pair with Y6. New small molecule donors are also designed taking into account quantum chemistry principles, using building units that are searched through similarity analysis. Their energy levels and power conversion efficiencies (PCEs) are predicted. Efficient small molecule donors with PCE > 13% are selected. This design and discovery pipeline provides an easy and fast way to select potential candidates for experimental work.
Conflict of Interest
The authors declare no conflict of interest.
Open Research
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Supporting Information
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Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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