Dataset for "The Catalytic Enantioselective [1,2]-Wittig Rearrangement Cascade of Allylic Ethers"
This data set includes output files from the quantum chemical calculations run with Gaussian16 (Revision C.01) that support our computational mechanistic study of the enantioselective [1,2]-Wittig rearrangement of allylic ethers. It also contains three sets of in situ reaction monitoring data (collected by University of St Andrews contributors) and a Python script that fits the rate constants of a first-order kinetics model to the experimental data.
Cite this dataset as:
Allsop, S.,
Lewis-Atwell, T.,
Farrar, E.,
Grayson, M.,
2025.
Dataset for "The Catalytic Enantioselective [1,2]-Wittig Rearrangement Cascade of Allylic Ethers".
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-01337.
Export
Data
Enantioselective_12-Wittig.zip
application/zip (408MB)
Creative Commons: Attribution 4.0
Zip file containing Gaussian files plus three sets of in situ reaction monitoring data (collected by University of St Andrews contributors) and a Python script.
Creators
Sam Allsop
University of Bath
Toby Lewis-Atwell
University of Bath
Elliot Farrar
University of Bath
Matt Grayson
University of Bath
Contributors
Tengfei Kang
Researcher
University of St Andrews
Justin O'Yang
Researcher
University of St Andrews
Kevin Kasten
Researcher
University of St Andrews
Martin Juhl
Researcher
University of St Andrews
David B. Cordes
Researcher
University of St Andrews
Aidan McKay
Researcher
University of St Andrews
Andrew D. Smith
Researcher
University of St Andrews
Documentation
Data collection method:
Structures were computed in Gaussian 16 (revision C.01) with ONIOM. The full DFT single point energies were also run in Gaussian 16 (revision C.01). 1H NMR spectroscopy was used to collect the in situ reaction monitoring data.
Funders
UK Research and Innovation
https://doi.org/10.13039/100014013
UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI
EP/S023437/1
Engineering and Physical Sciences Research Council
https://doi.org/10.13039/501100000266
DTP 2018-19 University of Bath
EP/R513155/1
Engineering and Physical Sciences Research Council (EPSRC)
https://doi.org/10.13039/501100000266
Machine Learning and Molecular Modelling: A Synergistic Approach to Rapid Reactivity Prediction
EP/W003724/1
Publication details
Publication date: 4 September 2025
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-01337
URL for this record: https://researchdata.bath.ac.uk/1337
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Contact person: Matt Grayson
Faculty of Science
Chemistry
