Show simple item record

dc.contributor.authorEibeck, Andreas
dc.contributor.authorNurkowski, Daniel
dc.contributor.authorMenon, Angiras
dc.contributor.authorBai, Jiaru
dc.contributor.authorWu, Jinkui
dc.contributor.authorZhou, Li
dc.contributor.authorMosbach, Sebastian
dc.contributor.authorAkroyd, Jethro
dc.contributor.authorKraft, Markus
dc.date.accessioned2021-10-29T02:12:46Z
dc.date.available2021-10-29T02:12:46Z
dc.date.issued2021-09-06
dc.identifier.issn2470-1343
dc.identifier.otherPMC8459373
dc.identifier.other34568656
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330022
dc.descriptionFunder: Cambridge Trust
dc.descriptionFunder: National Research Foundation Singapore
dc.descriptionFunder: Alexander von Humboldt-Stiftung
dc.descriptionFunder: China Scholarship Council
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.
dc.languageeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2470-1343
dc.sourcenlmid: 101691658
dc.titlePredicting Power Conversion Efficiency of Organic Photovoltaics: Models and Data Analysis.
dc.typeArticle
dc.date.updated2021-10-29T02:12:45Z
prism.endingPage23775
prism.issueIdentifier37
prism.publicationNameACS omega
prism.startingPage23764
prism.volume6
dc.identifier.doi10.17863/CAM.77466
rioxxterms.versionofrecord10.1021/acsomega.1c02156
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidNurkowski, Daniel [0000-0002-4983-8715]
dc.contributor.orcidBai, Jiaru [0000-0002-1246-1993]
dc.contributor.orcidWu, Jinkui [0000-0002-4839-338X]
dc.contributor.orcidZhou, Li [0000-0003-3539-7573]
dc.contributor.orcidMosbach, Sebastian [0000-0001-7018-9433]
dc.contributor.orcidAkroyd, Jethro [0000-0002-2143-8656]
dc.contributor.orcidKraft, Markus [0000-0002-4293-8924]
datacite.issupplementedby.urlhttps://doi.org/10.17863/CAM.65173


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International