MODNet, the Material Optimal Descriptor Network for materials properties prediction.
Project description
MODNet: Material Optimal Descriptor Network
Table of contents
- Introduction
- How to install
- Usage
- Pretrained models
- Stored MODData
- Documentation
- Getting started
- Author
- License
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.
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:
-
Machine learning materials properties for small datasets, De Breuck et al. (2020), arXiv:2004.14766.
-
Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet, De Breuck et al. (2021), arXiv:2102.02263.
-
MatBench benchmarking data repository: ml-evs/modnet-matbench.
How to install
MODNet can be installed via pip:
pip install modnet
Documentation
The documentation is available at ReadTheDocs.
Author
The first versions of this software were written by Pierre-Paul De Breuck, with contributions from Matthew Evans (v0.1.7+).
License
MODNet is released under the MIT License.
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