Revisiting the paradigm of reaction optimization in flow with a priori computational reaction intelligence

The use of micro/meso-fluidic reactors has resulted in both new scenarios for chemistry and new requirements for chemists. Through flow chemistry, large-scale reactions can be performed in drastically reduced reactor sizes and reaction times. This obvious advantage comes with the concomitant challenge of re-designing long-established batch processes to fit these new conditions. The reliance on experimental trial-and-error to perform this translation frequently makes flow chemistry unaffordable, thwarting initial aspirations to revolutionize chemistry. By combining computational chemistry and machine learning, we have developed a model that provides predictive power tailored specifically to flow reactions. We show its applications to translate batch to flow, to provide mechanistic insight, to contribute reagent descriptors, and to synthesize a library of novel compounds in excellent yields after executing a single set of conditions., This data set concerns the characterization and identification of new organic compounds reported in this manuscript. 
Please refer to the Supplementary Materials for details.
NMR data
1H and 13C NMR spectra were recorded with a Bruker Avance III 400 MHz NMR spectrometer using residual solvent peaks as an internal standard (1H NMR: CDCl3 at 7.26 ppm, TMS at 0.00 ppm. 13C NMR: CDCl3 at 77.16 ppm). Chemical shifts (δ) were reported in ppm and coupling constants (J) were reported in Hertz (Hz). Multiplicities were reported as singlet (s), doublet (d), triplet (t), q (quadruplet) and multiplet (m).
HPLC
Quantification of yields and conversions were done through HPLC analyses that were performed on a Shimadzu LC-2050C system equipped with a Diode Array Detector (DAD). The eluents used were: A: Water; B: Acetonitrile.
X-ray Diffraction
X-ray intensity data were collected at 100 K, on a Rigaku Oxford Diffraction Supernova Dual Source (Cu at zero) diffractometer equipped with an Atlas CCD detect..., NMR data
Topsin or Mestrenova
X-ray Diffraction
enCIFer
All other files
All other files are pdf or Word files, # Nitrosoarenes, Silyl Enol Ethers and Silylated α-Hydroxylaminated Ketones Characterization

## DOI: 10.5061/dryad.3ffbg79q3

Each folder contains structural and identification data of novel compounds prepared during the following related work: [https://onlinelibrary.wiley.com/doi/10.1002/anie.202311526](https://onlinelibrary.wiley.com/doi/10.1002/anie.202311526). The chemical species can be either nitrosoarenes, silyl enol ethers or silylated α-hydroxylaminated ketones (please refer to the manuscript for compound numbering). For each compound, we provide (a) unprocessed NMR data, (b) HPLC reports, (c) optionally HRMS data for new compounds and (d) optionally X-ray diffraction structure determination for compounds 1g and 3a (please also refer to the Cambridge Crystallographic Data Centre (CCDC 2279232-2279233)).

## Description of the data and file structure

Each folder is characterized with the following description.
Name of the folder: Compound numbering from the manuscript
Subfolde...