@inproceedings{030d3cd937a0432b995db91212b1f733,
title = "Predicting Optical Bandgaps in C60 and Functionalized Derivatives from Limited Data for Renewable Energy Applications",
abstract = "Accurate optical bandgap prediction is essential for advancing renewable energy materials, but data scarcity and complex non-linear dependencies limit traditional approaches, especially for C60-based materials in organic photovoltaics. We propose a novel data-efficient machine learning framework that integrates Gaussian Process Regression (GPR) with Density Functional Theory (DFT) simulations to predict absorbance spectra and compute optical bandgaps from limited data. Our approach leverages Bayesian inference for uncertainty quantification, significantly reducing reliance on computationally expensive DFT calculations while maintaining accuracy. We demonstrate that our method generalizes across C60 and functionalized derivatives, enabling scalable and cost-effective bandgap prediction with minimal training samples.",
keywords = "AI for Science, Density Functional Theory, Gaussian Process Regression, Optimal Bandgap Prediction, Small Data Learning",
author = "Mehmet Tuncel and Hasan Kurban and Erchin Serpedin 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-POWERENG63314.2025.11027245",
language = "English",
isbn = "979-8-3315-1518-8",
series = "Compatibility Power Electronics And Power Engineering",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 Ieee 19th International Conference On Compatibility, Power Electronics And Power Engineering, Cpe-powereng",
address = "United States",
}