Omar Ömer H, Nematiaram Tahereh, Troisi Alessandro, Padula Daniele
University of Liverpool, Department of Chemistry, Liverpool, L69 7ZD, UK.
Università di Siena, Dipartimento di Biotecnologie, Chimica e Farmacia, Siena, 53100, Italy.
Sci Data. 2022 Feb 14;9(1):54. doi: 10.1038/s41597-022-01142-7.
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.
我们展示了一个包含48182种有机半导体的数据集,这些分子是通过有记录的合成途径制备的,并且在固态下稳定。我们的搜索基于剑桥结构数据库,通过计算筛选程序从中选择半导体。对于每个条目,我们提供了一组与有机材料研究相关的电子特性,以及用于进一步计算和/或分析的电子波函数。该数据集偏差较小,因为它不是基于一组为有机电子学设计的材料构建的,因此它为寻找已知材料的新应用提供了一个绝佳的起点,具有获得新颖物理见解的巨大潜力。该数据集包含在有机材料研究的许多领域中用作基准的分子,能够测试针对所需应用的计算筛选的可靠性,“重新发现”知名分子。这在有机材料领域的一系列不同应用中得到了证明,证实了已知有机分子重新利用的潜力。