Max Schwilk¹, Diana N. Tahchieva¹, O. Anatole von Lilienfeld^1*

1 Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland

* Corresponding authors emails: anatole.vonlilienfeld@unibas.ch

DOI10.24435/materialscloud:2020.0051/v1 [version v1]

Publication date: May 08, 2020

How to cite this record

Max Schwilk, Diana N. Tahchieva, O. Anatole von Lilienfeld, The QMspin data set: Several thousand carbene singlet and triplet state structures and vertical spin gaps computed at MRCISD+Q-F12/cc-pVDZ-F12 level of theory, Materials Cloud Archive 2020.0051/v1 (2020), https://doi.org/10.24435/materialscloud:2020.0051/v1

Description

High-quality data sets of free carbenes have remained unavailable in the scientific literature so far. We provide approximately 5k and 8k verified carbene structures in their respective singlet or triplet state. Vertical spin gaps have been computed at higher order multireference level of theory (MRCISD+Q-F12/cc-pVDZ-F12). The carbenes presented are all derived through double hydrogen abstraction from saturated carbon centers of a subset obtained by randomly sampling the chemical space of approximately 300k carbene candidates possible within closed shell organic molecules in the QM9 data set.

Materials Cloud sections using this data

Files

File name	Size	Description
QMspin_Part1.tar.gz MD5md5:ab9998be394bab780e2dc97eb2778b3a	291.1 MiB	This is a zipped tarball of the QMspin data set Part 1. For description see readme file.
QMspinReadme.txt MD5md5:09741a8a8632eec87fd3c61885bf115a	5.9 KiB	This is a readme file containing a description of the file structure of the QMspin data base Part 1.

License

Files and data are licensed under the terms of the following license: Materials Cloud non-exclusive license to distribute v1.0.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

carbenes machine learning chemical space spin gaps ERC MARVEL SNSF