Dataset for the evaluation of Supercritical Fluid Chromatography Polar Stationary Phases with OH moieties

Raw data used for the evaluation of supercritical fluid chromatography stationary phases with OH moieties published in article "Advancing Fundamental Understanding of Retention Interactions in Supercritical Fluid Chromatography Using Artificial Neural Networks: Polar Stationary Phases with OH moieties" in Analytical Chemistry, 2024. Data set contains: (i) chromatograms of 107 analytes measured on silica, hybrid silica, and diol column using methanol, 10 mmol/L ammonium in methanol, and 2% water in methanol as organic modifiers in 8 points during 1 year (Empower project, Excel sheets of retention times and measured mixtures), (ii) 226 molecular descriptors calculated by CDK Descriptor Calculator (v.1.4.8) from 3D structures of the 107 analytes optimized by semi-empirical AM1 quantum mechanical calculations using the MOPAC application of Chem 3D Pro version 14.0 software (CambridgeSoft) (Excel sheet), (iii) weights assigned to each molecular descriptor at each chromatographic conditions by artificial neural network created using the neural network simulator in Matlab R2023a with the deep learning toolbox V.23.2 (The MathWorks, Inc., Massachusetts, USA) and a sigmoid activation function, a backpropagation learning algorithm with 500 learning cycles (Excel sheet).