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MODNet, the Material Optimal Descriptor Network for materials properties prediction.

Project description

MODNet: Material Optimal Descriptor Network

arXiv Build Status Read the Docs

Introduction

This repository contains the Python (3.8) package implementing the Material Optimal Descriptor Network (MODNet). It is a supervised machine learning framework for learning material properties from either the composition or crystal structure. The framework is well suited for limited datasets and can be used for learning multiple properties together by using joint learning.

MODNet appears on the MatBench leaderboard. As of 11/11/2021, MODNet provides the best performance of all submitted models on 7 out of 13 tasks.

This repository also contains two pretrained models that can be used for predicting the refractive index and vibrational thermodynamics from any crystal structure.

See the MODNet papers and repositories below for more details:

MODNet schematic

Figure 1. Schematic representation of the MODNet.

How to install

First, create a virtual environment (e.g., named modnet) with Python 3.8:

conda create -n modnet python=3.8

activate the environment:

conda activate modnet

Then, install pymatgen v2020.8.13 with conda, which will bundle several pre-built dependencies (e.g., numpy, scipy):

conda install -c conda-forge pymatgen=2020.8.13

Finally, install MODNet from PyPI with pip:

pip install modnet

Documentation

The documentation is available at ReadTheDocs.

Author

This software was written by Pierre-Paul De Breuck and Matthew Evans with contributions from David Waroquiers and Gregoire Heymans. For an up-to-date list, see the Contributors on GitHub.

License

MODNet is released under the MIT License.

Project details


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modnet-0.1.12.tar.gz (10.3 MB view hashes)

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