TY - GEN
T1 - Predicting Optical Bandgaps in C60 and Functionalized Derivatives from Limited Data for Renewable Energy Applications
AU - Tuncel, Mehmet
AU - Kurban, Hasan
AU - Serpedin, Erchin
AU - Kurban, Mustafa
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/5/22
Y1 - 2025/5/22
N2 - 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.
AB - 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.
KW - AI for Science
KW - Density Functional Theory
KW - Gaussian Process Regression
KW - Optimal Bandgap Prediction
KW - Small Data Learning
UR - https://www.scopus.com/pages/publications/105009402150
U2 - 10.1109/CPE-POWERENG63314.2025.11027245
DO - 10.1109/CPE-POWERENG63314.2025.11027245
M3 - Conference contribution
AN - SCOPUS:105009402150
T3 - 2025 IEEE 19th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2025 - Proceedings
BT - 2025 IEEE 19th International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2025 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2025
Y2 - 20 May 2025 through 22 May 2025
ER -