Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160582
Title: Predicting power conversion efficiency of organic photovoltaics: models and data analysis
Authors: Eibeck, Andreas
Nurkowski, Daniel
Menon, Angiras
Bai, Jiaru
Wu, Jinkui
Zhou, Li
Mosbach, Sebastian
Akroyd, Jethro
Kraft, Markus
Keywords: Engineering::Chemical engineering
Issue Date: 2021
Source: Eibeck, A., Nurkowski, D., Menon, A., Bai, J., Wu, J., Zhou, L., Mosbach, S., Akroyd, J. & Kraft, M. (2021). Predicting power conversion efficiency of organic photovoltaics: models and data analysis. ACS Omega, 6(37), 23764-23775. https://dx.doi.org/10.1021/acsomega.1c02156
Journal: ACS omega
Abstract: In this paper, the ability of three selected machine learning neural and baseline models in predicting the power conversion efficiency (PCE) of organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentive fingerprints (attentive FP), and simple graph neural networks (simple GNN) as well as baseline support vector regression (SVR), random forests (RF), and high-dimensional model representation (HDMR) methods are trained to both the large and computational Harvard clean energy project database (CEPDB) and the much smaller experimental Harvard organic photovoltaic 15 dataset (HOPV15). It was found that the neural-based models generally performed better on the computational dataset with the attentive FP model reaching a state-of-the-art performance with the test set mean squared error of 0.071. The experimental dataset proved much harder to fit, with all of the models exhibiting a rather poor performance. Contrary to the computational dataset, the baseline models were found to perform better than the neural models. To improve the ability of machine learning models to predict PCEs for OPVs, either better computational results that correlate well with experiments or more experimental data at well-controlled conditions are likely required.
URI: https://hdl.handle.net/10356/160582
ISSN: 2470-1343
DOI: 10.1021/acsomega.1c02156
Schools: School of Chemical and Biomedical Engineering 
Organisations: Cambridge Centre for Advanced Research and Education
Rights: © 2021 The Authors. Published by American Chemical Society. This is an open-access article distributed under the terms of the Creative Commons Attribution License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

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