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QMrxn20: Thousands of reactants and transition states for competing E2 and SN2 reactions

Guido Falk von Rudorff1, Stefan N. Heinen1, Marco Bragato1, O. Anatole von Lilienfeld1*

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:sf-tz [version v1]

Publication date: Jun 09, 2020

How to cite this record

Guido Falk von Rudorff, Stefan N. Heinen, Marco Bragato, O. Anatole von Lilienfeld, QMrxn20: Thousands of reactants and transition states for competing E2 and SN2 reactions, Materials Cloud Archive 2020.55 (2020), https://doi.org/10.24435/materialscloud:sf-tz


For competing E2 and SN2 reactions, we report 4'400 validated transition state geometries and 143'200 reactant complex geometries including conformers obtained at MP2/6-311G(d) and DF-LCCSD/cc-pVTZ//MP2/6-311G(d) level of theory. The data covers the chemical compound space spanned by the substituents NO2, CN, CH3, and NH2 and early halogens (F, Cl, Br) as nucleophiles and leaving groups based on an ethane skeleton. Ready-to-use activation energies are given for the different levels of theory where in some cases relaxation effects have been treated with machine learning surrogate models.

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File name Size Description
2.5 KiB Detailed description of the data set
4.6 MiB Energies of individual geometries (transition states, reactants, products)
459.9 KiB Activation energies
78.9 MiB All geometries as XYZ files


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

Journal reference
G. F. von Rudorff, S. N. Heinen, M. Bragato, O. A. von Lilienfeld, Machine Learning: Science and Technology 1, 045026 (2020). doi:10.1088/2632-2153/aba822
Preprint (Preprint where the data generation is discussed)


reaction organic molecules chemical space activation energies conformers machine learning ERC MARVEL SNSF

Version history:

2020.55 (version v1) [This version] Jun 09, 2020 DOI10.24435/materialscloud:sf-tz