[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124462-en":3,"doc-seo-124462-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124462,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine Learning–Based Prediction of Organic Solar Cell Performance Using Molecular Descriptors","Organic solar cell performance depends directly on donor and acceptor molecular properties, including electronic and structural characteristics. This study applies machine learning models—GRNN, SVM, and Tree Boost—to predict key photovoltaic metrics such as PCE, JSC, VOC, and FF using an experimentally compiled dataset. Correlation analysis links performance to descriptors like polarizability, bandgap, dipole moment, and charge transfer. Model comparison shows GRNN yields lower prediction errors on the considered dataset. Sensitivity analysis evaluates variable importance and GRNN kernel influence, supporting data-driven screening for OSCs.","Machine Learning–Based Prediction of Organic Solar Cell Performance Using Molecular Descriptors  \nMohammed Saleh Alshaikh  \nDevice Simulation Laboratory, Department of Electrical Engineering, College of Engineering and Architecture, Umm Al-Qura University, Makkah, [Saudi Arabia. ](Saudi Arabia. msshaikh@uqu.edu.sa)[msshaikh@uqu.edu.sa](Saudi Arabia. msshaikh@uqu.edu.sa), ORCID ID: 0000-0003-1152-3731  \nAbstract: The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.  \nKeywords: General Regression Neural Networks, Organic Solar Cells, Power Conversion Efficiency, Sensitivity Analysis, Support Vector Machine, Tree Boost.  \n1 INTRODUCTION  \nTraditional fossil energy sources are increasingly unable to meet the sustainable development needs of human society. This inadequacy stems from rising global energy consumption and the environmental impacts associated with fossil fuel extraction and combustion. In recent years, data-driven and machine-learning-based approaches have been increasingly explored to address complex challenges in energy materials research, including predicting photovoltaic device performance using experimentally reported datasets [1]. The harmful effects of fossil fuels, including greenhouse gas emissions and air pollution, further emphasize the urgent need for a transition toward cleaner and more sustainable energy alternatives [2]. Among renewable energy technologies, solar electricity has emerged as one of the most promising options. A solar cell, or photovoltaic device, converts light energy directly into electrical energy through the photovoltaic effect [3]-[5] . This process involves the absorption of photons by a semiconductor material, resulting in the generation of charge carriers that can be collected as electrical current [6] .  \nSemiconductor materials, both organic and inorganic, play a crucial role in photon-to-electron conversion. Organic solar cells (OSCs) have attracted considerable attention due to their lightweight nature, mechanical flexibility, and potential for low-cost fabrication through solution processing and roll-to-roll manufacturing techniques [7][8]. Despite these advantages, OSCs have yet to achieve widespread commercial deployment, primarily due to their relatively lower PCE and limited operational stability compared to inorganic counterparts. Recent advances in material design and device architecture have enabled OSCs to achieve PCEs of approximately 13%[9]-[11]; however, this performance remains below that of mature inorganic photovoltaic technologies. Further improvements in OSC performance are hindered by the complexity of organic material synthesis and purification, as well as the extensive experimental effo","cbCaivcO6q3lvnm6","https://ap.wps.com/l/cbCaivcO6q3lvnm6","pdf",1267534,1,17,"English","en",105,"# Introduction\n## Motivation for machine learning in energy materials\n## Role of organic solar cells and key performance metrics\n## Challenges in predicting performance from molecular structure\n# Related work\n## Descriptor-based machine learning in organic photovoltaics\n## Applications of ML in molecular and materials domains\n# Study objective and approach","[{\"question\":\"Which machine learning models are used to predict organic solar cell performance in the study?\",\"answer\":\"The study uses GRNN, SVM, and Tree Boost to predict multiple OSC performance metrics including PCE, JSC, VOC, and FF.\"},{\"question\":\"What types of descriptors and variables are analyzed for their relationship with OSC performance?\",\"answer\":\"Correlation and sensitivity analyses examine molecular/material characteristics such as polarizability, bandgap, dipole moment, and charge transfer, and evaluate the importance of predictor variables.\"},{\"question\":\"How is the dataset for model training and evaluation obtained?\",\"answer\":\"The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al.\"}]","Machine Learning–Based Prediction of Organic Solar Cell Performance Using Molecular Descriptors | 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machine learning models are used to predict organic solar cell performance in the study?","Question",{"text":75,"@type":76},"The study uses GRNN, SVM, and Tree Boost to predict multiple OSC performance metrics including PCE, JSC, VOC, and FF.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What types of descriptors and variables are analyzed for their relationship with OSC performance?",{"text":80,"@type":76},"Correlation and sensitivity analyses examine molecular/material characteristics such as polarizability, bandgap, dipole moment, and charge transfer, and evaluate the importance of predictor variables.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the dataset for model training and evaluation obtained?",{"text":84,"@type":76},"The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et 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