Photovoltaic conversion of organic photovoltaic materials based on molecular fingerprint feature engineering

Hongjie Chen; Xuanyu Zhang; Zengyuan Chen

doi:10.1117/12.3025045

8 February 2024 Photovoltaic conversion of organic photovoltaic materials based on molecular fingerprint feature engineering

Hongjie Chen, Xuanyu Zhang, Zengyuan Chen

Author Affiliations +

Proceedings Volume 13066, International Conference on Optoelectronic Materials and Devices (ICOMD 2023); 1306609 (2024) https://doi.org/10.1117/12.3025045
Event: 2023 International Conference on Optoelectronic Materials and Devices (COMD 2023), 2023, Chongqing, China

Abstract

In recent years, organic solar cells (OSCs) have gradually become the focus of renewable energy research. In order to predict the photovoltaic characteristics of OSCs more accurately and efficiently, researchers have incorporated numerous machine learning models into their studies. In this research, we designed and implemented a neural network model based on molecular fingerprints and applied it to the study of the power conversion efficiency (PCE) prediction of OSCs. We explored the impact of different organic photovoltaic material structures and feature extraction methods on the prediction of the PCE. Through experimental evaluations, the model not only achieved good experimental results in terms of material structure and PCE prediction but also compared the feature extraction methods of different molecular fingerprints. It was found that both Morgan and Circular fingerprints performed excellently in multiple scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongjie Chen, Xuanyu Zhang, and Zengyuan Chen "Photovoltaic conversion of organic photovoltaic materials based on molecular fingerprint feature engineering", Proc. SPIE 13066, International Conference on Optoelectronic Materials and Devices (ICOMD 2023), 1306609 (8 February 2024); https://doi.org/10.1117/12.3025045

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KEYWORDS

Organic photovoltaics

Machine learning

Data modeling

Performance modeling

Neural networks

Education and training

Photovoltaics

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