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This package can give predictions on stability of new perovskite compoounds using machine learning approach.

How to use:

  1. New compounds info should be put in the newCompound.xlsx file, with the specified format as described in the excel file
  2. The training data should be put in the perovskite_DFT_EaH_FormE.xlsx. This file already contains over 1900 compounds. Users can add more compounds to this training set.
  3. Run python script energy_prediction.py using command: python energy_prediction.py. Requirements:

  • python version >= 3.5

  • sklearn, pandas, numpy.

  1. During the running of the python script, temporary files *.csv will be generated, which contain the generated features of the compounds in the training set and the new compounds to be predicted. The temporary files are generated by feature_vector.py, called by the energy_prediction.py.
  2. Results are put in the prediction_result.xlsx file. In the stability column, 1 stands for stable, 0 stands for unstable.

Information:

The energy_prediction embeds the selected best classification model (extra trees) and the best regression model (kernel ridge regression).

The elemental property database is provided as continousproperty.xlsx, discrictproperty.xlsx and shannon_perovskite.xlsx. Feature inportance list is stored by *.txt files.