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Predicting Mechanochemical Co-crystallization via Machine Learning.ipynb

code archive 11304 disk0/00/00/09/63 2021-06-28 10:32:16 2024-05-14 13:12:12 2021-06-28 10:32:16 data_collection show Gröls Jan jrg70@bath.ac.uk 0000-0003-0843-9408 University of Bath TRUE Castro Dominguez Bernardo B.Castro.Dominguez@bath.ac.uk 0000-0001-5913-305X University of Bath FALSE DataCollector Gröls Jan jrg70@bath.ac.uk 0000-0003-0843-9408 University of Bath CM0060 dept_chem_eng Co-crystal(s), Crystal Engineering, Machine Learning, Computational pharmaceutical discovery The entire data supports the research publication and can be divided in 3 parts: i) Training data (Raw PXRD data and its description of the labelling contained in the Documentation.zip), ii) Excel-files that are the input of the algorithm (Results.xlsx contains reaction outcomes/ Chem.xlsx contains all chemical descriptors for used molecules; both are in the Documentation.zip) iii) Code (Python algorithm that uses the excel-files from the previous point to generate predictive capabilities) 2021-06-27 University of Bath public RightsHolder University of Bath University of Bath https://doi.org/10.13039/501100000835 cent_sus_tech The methodology is described in the supporting paper. en 1 10.15125/BATH-00963 https://doi.org/10.1016/j.compchemeng.2021.107416 pub open