What is XenonPy project

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Overview

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). This package still under hard working. The current release provides some limited features:

  • Interface to the public materials database

  • Library of materials descriptors (compositional/structural descriptors)

  • pre-trained model library XenonPy.MDL (v0.1.0.beta, 2019/8/9: more than 140,000 models (include private models) in 35 properties of small molecules, polymers, and inorganic compounds)

  • Machine learning tools.

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

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Features

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

Reference

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.

Contributing

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.