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Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds

We present a data set of 48182 organic semiconductors, constituted of molecules that were prepared with a documented synthetic pathway and are stable in solid state. We based our search on the Cambridge Structural Database, from which we selected semiconductors with a computational funnel procedure....

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Autores principales: Omar, Ömer H., Nematiaram, Tahereh, Troisi, Alessandro, Padula, Daniele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844419/
https://www.ncbi.nlm.nih.gov/pubmed/35165288
http://dx.doi.org/10.1038/s41597-022-01142-7
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author Omar, Ömer H.
Nematiaram, Tahereh
Troisi, Alessandro
Padula, Daniele
author_facet Omar, Ömer H.
Nematiaram, Tahereh
Troisi, Alessandro
Padula, Daniele
author_sort Omar, Ömer H.
collection PubMed
description We present a data set of 48182 organic semiconductors, constituted of molecules that were prepared with a documented synthetic pathway and are stable in solid state. We based our search on the Cambridge Structural Database, from which we selected semiconductors with a computational funnel procedure. For each entry we provide a set of electronic properties relevant for organic materials research, and the electronic wavefunction for further calculations and/or analyses. This data set has low bias because it was not built from a set of materials designed for organic electronics, and thus it provides an excellent starting point in the search of new applications for known materials, with a great potential for novel physical insight. The data set contains molecules used as benchmarks in many fields of organic materials research, allowing to test the reliability of computational screenings for the desired application, “rediscovering” well-known molecules. This is demonstrated by a series of different applications in the field of organic materials, confirming the potential for the repurposing of known organic molecules.
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spelling pubmed-88444192022-03-04 Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds Omar, Ömer H. Nematiaram, Tahereh Troisi, Alessandro Padula, Daniele Sci Data Data Descriptor We present a data set of 48182 organic semiconductors, constituted of molecules that were prepared with a documented synthetic pathway and are stable in solid state. We based our search on the Cambridge Structural Database, from which we selected semiconductors with a computational funnel procedure. For each entry we provide a set of electronic properties relevant for organic materials research, and the electronic wavefunction for further calculations and/or analyses. This data set has low bias because it was not built from a set of materials designed for organic electronics, and thus it provides an excellent starting point in the search of new applications for known materials, with a great potential for novel physical insight. The data set contains molecules used as benchmarks in many fields of organic materials research, allowing to test the reliability of computational screenings for the desired application, “rediscovering” well-known molecules. This is demonstrated by a series of different applications in the field of organic materials, confirming the potential for the repurposing of known organic molecules. Nature Publishing Group UK 2022-02-14 /pmc/articles/PMC8844419/ /pubmed/35165288 http://dx.doi.org/10.1038/s41597-022-01142-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Omar, Ömer H.
Nematiaram, Tahereh
Troisi, Alessandro
Padula, Daniele
Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds
title Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds
title_full Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds
title_fullStr Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds
title_full_unstemmed Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds
title_short Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds
title_sort organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844419/
https://www.ncbi.nlm.nih.gov/pubmed/35165288
http://dx.doi.org/10.1038/s41597-022-01142-7
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