Dataset supporting the paper: Mechanochemical Co-crystallization: Insights and Predictions

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,
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
iii) Code (Python algorithm that uses the excel-files from the previous point to generate predictive capabilities)

Co-crystal(s), Crystal Engineering, Machine Learning, Computational pharmaceutical discovery
Chemical synthesis

Cite this dataset as:
Gröls, J., Castro Dominguez, B., 2021. Dataset supporting the paper: Mechanochemical Co-crystallization: Insights and Predictions. Bath: University of Bath Research Data Archive. Available from:


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application/zip (3MB)
Creative Commons: Attribution 4.0

PXRD Raw data for the training data set, for description please see the excel file Raw_numbering.xlsx


Predicting … Machine Learning.ipynb
text/plain (18kB)
Creative Commons: Attribution 4.0

Python file of the written code


Jan Gröls
Data Collector
University of Bath


University of Bath
Rights Holder


Data collection method:

The methodology is described in the supporting paper.

Documentation Files
application/zip (786kB)
Creative Commons: Attribution 4.0

Description for the raw data as well as experimental outcomes


Publication details

Publication date: 27 June 2021
by: University of Bath

Version: 1


URL for this record:

Related papers and books

Gröls, J. R., and Castro-Dominguez, B., 2021. Mechanochemical co-crystallization: Insights and predictions. Computers & Chemical Engineering, 153, 107416. Available from:

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: Jan Gröls


Faculty of Engineering & Design
Chemical Engineering

Research Centres & Institutes
Centre for Sustainable and Circular Technologies (CSCT)