What is XenonPy project

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XenonPy is a Python library that implements a comprehensive set of machine learning tools for materials informatics. Its functionalities partially depend on Python (PyTorch) and R (MXNet). The current release (v0.2.1, 2019/2/20) is a prototype version, which provides some limited modules:

  • Interface to the public materials database

  • Library of materials descriptors (compositional/structural descriptors)

  • Pretrained model library XenonPy.MDL (v0.1.0b, 2018/12/25: more than 10,000 models in 35 properties of small molecules, polymers, and inorganic compounds)

  • Machine learning tools.

  • Transfer learning using the pre-trained models in XenonPy.MDL



XenonPy has a rich set of tools for various materials informatics applications. The descriptor generator class can calculate several types of numeric descriptors from compositional, structure. By using XenonPy’s built-in visualization functions, the relationships between descriptors and target properties can be easily shown in a heatmap.

Transfer learning is an important tool for the efficient application of machine learning methods to materials informatics. To facilitate the widespread use of transfer learning, we have developed a comprehensive library of pre-trained models, called XenonPy.MDL. This library provides a simple API that allows users to fetch the models via an HTTP request. For the ease of using the pre-trained models, some useful functions are also provided.

See Features for details


Yamada, H., Liu, C., Wu, S., Koyama, Y., Ju, S., Shiomi, J., Morikawa, J., Yoshida, R. Transfer learning: a key driver of accelerating materials discovery with machine learning, in preparation.


XenonPy is an open source project inspired by matminer.
This project is under on-going development. We would appreciate any feedback from the users.
Code contributions are also very welcomed. See Contribution guidelines for more details.