Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160582
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dc.contributor.authorEibeck, Andreasen_US
dc.contributor.authorNurkowski, Danielen_US
dc.contributor.authorMenon, Angirasen_US
dc.contributor.authorBai, Jiaruen_US
dc.contributor.authorWu, Jinkuien_US
dc.contributor.authorZhou, Lien_US
dc.contributor.authorMosbach, Sebastianen_US
dc.contributor.authorAkroyd, Jethroen_US
dc.contributor.authorKraft, Markusen_US
dc.date.accessioned2022-07-27T02:57:05Z-
dc.date.available2022-07-27T02:57:05Z-
dc.date.issued2021-
dc.identifier.citationEibeck, 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.1c02156en_US
dc.identifier.issn2470-1343en_US
dc.identifier.urihttps://hdl.handle.net/10356/160582-
dc.description.abstractIn 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.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relation.ispartofACS omegaen_US
dc.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.en_US
dc.subjectEngineering::Chemical engineeringen_US
dc.titlePredicting power conversion efficiency of organic photovoltaics: models and data analysisen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Chemical and Biomedical Engineeringen_US
dc.contributor.organizationCambridge Centre for Advanced Research and Educationen_US
dc.identifier.doi10.1021/acsomega.1c02156-
dc.description.versionPublished versionen_US
dc.identifier.pmid34568656-
dc.identifier.scopus2-s2.0-85115212368-
dc.identifier.issue37en_US
dc.identifier.volume6en_US
dc.identifier.spage23764en_US
dc.identifier.epage23775en_US
dc.subject.keywordsSolar-Cellsen_US
dc.subject.keywordsClean Energy Projecten_US
dc.description.acknowledgementThis research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. J.B. acknowledges financial support provided by CSC Cambridge International Scholarship from the Cambridge Trust and China Scholarship Council. M.K. gratefully acknowledges the support of the Alexander von Humboldt Foundation. The authors are grateful to EPSRC (grant number: EP/R029369/1) and ARCHER for financial and computational support as a part of their funding to the UK Consortium on Turbulent Reacting Flows (www.ukctrf.com).en_US
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