This dataverse contains all data required to replicate the analysis conducted in Muellers et al.'s 2026 publication "Using NMR to predict molecular reactivity" (DOI). Several items below were sourced from other open source datasets, as indicated.
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Oct 12, 2026
Muellers, Tobias, 2026, "Replication code for "Using NMR to predict molecular reactivity"", https://doi.org/10.60600/YU/5VVXU0, Yale Dataverse, V1
Upload of files located at https://github.com/TDMuellers/NMR2FMO, as of October 12, 2026.
Python Source Code - 18.3 KB - MD5: 429ceb918aef48e4ce3f798e33440930
PNG Image - 458.2 KB - MD5: 9b2664aaf95667f12c4478b379345f6c
Plain Text - 1.0 KB - MD5: 646929022647a3346ff560176d58837a
Jupyter Notebook - 198.4 KB - MD5: 211685abb6c2153d83f5a09650c7264c
Markdown Text - 1.4 KB - MD5: a1ecfd788f2638e321a7a2a03573d56b
Sep 15, 2026
Muellers, Tobias, 2026, "ωB97X-D-computed HOMO-LUMO energy gaps for curated NMRShiftDB2 molecules: files 0-1,000", https://doi.org/10.60600/YU/AAJPLH, Yale Dataverse, V1
.log files for completed DFT calculations using the ωB97X-D functional and def2svp basis set58 as implemented in Gaussian 16: files 0-1,000
Unknown - 515.3 KB - MD5: ab574b5e538dc41de0d1a9839ab73155
Unknown - 411.3 KB - MD5: 49f2fbdc5d923c050cc80420e900a76a
Unknown - 778.2 KB - MD5: 2e79f550442cfe2f77f063cddfaf1bbd
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