Cargando…
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....
Autores principales: | , , , |
---|---|
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 |
_version_ | 1784651471401254912 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8844419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT omaromerh organicmaterialsrepurposingadatasetfortheoreticalpredictionsofnewapplicationsforexistingcompounds AT nematiaramtahereh organicmaterialsrepurposingadatasetfortheoreticalpredictionsofnewapplicationsforexistingcompounds AT troisialessandro organicmaterialsrepurposingadatasetfortheoreticalpredictionsofnewapplicationsforexistingcompounds AT paduladaniele organicmaterialsrepurposingadatasetfortheoreticalpredictionsofnewapplicationsforexistingcompounds |