Prediction of photoelectric conversion efficiency of organic photovoltaic materials based on deep learning

Xiangyu Kong; Bohao Xu

doi:10.1117/12.3007035

25 October 2023 Prediction of photoelectric conversion efficiency of organic photovoltaic materials based on deep learning

Xiangyu Kong, Bohao Xu

Author Affiliations +

Proceedings Volume 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023); 1280114 (2023) https://doi.org/10.1117/12.3007035
Event: Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 2023, Dalian, China

Abstract

The energy crisis has gradually been reflected in all aspects. It is urgent to develop efficient and clean new energy to reduce the dependence on fossil energy. As a kind of material which can directly convert solar energy or other light energy into electric energy, organic photovoltaic material is becoming a kind of low-carbon energy material with great application prospect. While machine learning can improve the efficiency of material design in the search for new high performance organic photovoltaic materials, its predictive power is severely limited by developing and selecting descriptors. An end-to-end deep learning model has been constructed to predict the photoelectric conversion efficiency of organic photovoltaic materials using algorithms such as cyclic neural networks, convolutional neural networks, and graph neural networks. The constructed model can directly extract the structural information of compounds from Simplified Molecular Input Line Entry System (SMILES) symbols, molecular images and molecular graph networks, without the need to manually develop and select descriptors. Our model can not only accurately predict the photoelectric conversion efficiency of organic photovoltaic materials (the determination coefficients of the optimal model and the test set prediction are both > 0.73) and can identify key structural features that affect conversion efficiency. The research results can provide solutions for the design of new photovoltaic materials.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangyu Kong and Bohao Xu "Prediction of photoelectric conversion efficiency of organic photovoltaic materials based on deep learning", Proc. SPIE 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 1280114 (25 October 2023); https://doi.org/10.1117/12.3007035

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Organic photovoltaics

Data modeling

Neural networks

Deep learning

Data conversion

Education and training

Convolutional neural networks

Show All Keywords

Keywords/Phrases

Search In:

Publication Years