@inproceedings{f12d72700f5d4b7c8049ed569f5a3e9e,
title = "Instance-Based Learning-Driven Density of States Analysis in Functionalized Fullerene Derivatives for Optimizing Organic Photovoltaics",
abstract = "We employ instance-based machine learning to evaluate functionalized fullerene derivatives as electron-acceptor materials for organic photovoltaics. Using density of states (DOS) data from seven fullerene-based models, we identify key electronic properties that influence photovoltaic efficiency. Our results demonstrate that k-nearest neighbor methods achieve superior predictive accuracy, underscoring the potential of instance-based learning in accelerating material discovery and optimization for next-generation photovoltaics.",
keywords = "AI for science, Density of states, Fullerene derivatives, Organic photovoltaics",
author = "Parichit Sharma and Hasan Kurban and Mehmet Dalkilic and Mustafa Kurban",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 19th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2025 ; Conference date: 20-05-2025 Through 22-05-2025",
year = "2025",
month = may,
day = "22",
doi = "10.1109/CPE-POWERENG63314.2025.11027195",
language = "English",
series = "2025 IEEE 19th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2025 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 IEEE 19th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2025 - Proceedings",
address = "United States",
}