Official site | https://github.com/yoshida-lab/XenonPy |
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Openness | ★★★ |
License |
BSD 3-Clause License |
Core Developers |
Liu Chang(NIMS), Ryo Yoshida(ISM), Yukinori Koyama(NIMS) |
Availability |
Python>=3.5, PyTorch>=0.4 |
Related Sites |
MateriApps: https://ma.issp.u-tokyo.ac.jp/en/app/1435 |
Forum | https://ma.issp.u-tokyo.ac.jp/en/app/1435 |
Other |
External Link
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XenonPy
XenonPy is a Python library with implemented machine learning tools for materials informatics. It provides a library to compute material descriptors using compositional, structural, and RDKit descriptors. The library also has a heat map visualization function to help users easily understand the correlation between the descriptors and the desired properties. Pre-trained models using neural networks are available to predict various compound properties, and a framework for transition learning is also provided. Combining with iqspr, it is also possible to search for unknown materials with desired properties.
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