If you had any questions related to matminer, please consult the official documentation. If the documentation did not cover your question, please look for it in matminer forum and if not already answered, post your question there as others may have the same question. If you found it useful in your work, please cite matminer paper.
The purpose of this tutorial is to provide examples of how to use different aspects of the materials data minining python package, matminer, from gathering the data to featurization, visualization and training machine learning models to predict materials properties.
You may be going through these notebooks via binder in which case you can double-click on any given cell and change the source and then use shift + enter to run that cell and see the result of your change. Do not worry as binder creates your own isolated environment so you are not really changing any source code. Alternatively, you can clone the matminer_examples Github repository to run these notebooks on your machine and also checkout the examples that are written in simple python script which are not shown here.
Finally, having a basic knowledge of python programming language and Pandas (spreadsheet for python) would maximize the benefits of these examples.
In these notebooks we show how to use matminer to retrieve materials data (e.g. composition, band gaps), generate features for materials (e.g. using their formula or crystal structures), visualize, train machine learning models, interpret and evaluate those models.
Note: "Advanced" notebooks refer to those that require some knowledge of pandas or scikit-learn.
More visualization examples (in Python script rather than Juypter notebook format) can be found in the figrecipes-py folder of the examples directory.