maml 2021.3.2

Features

1. Convert materials (crystals and molecules) into features. In addition to common compositional, site and structural features, we provide the following fine-grain local environment features.

1. Bispectrum coefficients

2. Behler Parrinello symmetry functions

3. Smooth Overlap of Atom Position (SOAP)

4. Graph network features (composition, site and structure)

2. Use ML to learn relationship between features and targets. Currently, the maml supports sklearn and keras models.

3. Applications:

1. pes for modelling the potential energy surface, constructing surrogate models for property prediction.

1. Neural Network Potential (NNP)

2. Gaussian approximation potential (GAP) with SOAP features

3. Spectral neighbor analysis potential (SNAP)

4. Moment Tensor Potential (MTP)

2. rfxas for random forest models in predicting atomic local environments from X-ray absorption spectroscopy.

Installation

Pip install via PyPI:

pip install maml

To run the potential energy surface (pes), lammps installation is required you can install from source or from conda:

conda install -c conda-forge/label/cf202003 lammps

The SNAP potential comes with this lammps installation. The GAP package for GAP and MLIP package for MTP are needed to run the corresponding potentials. For fitting NNP potential, the n2p2 package is needed.

Install all the libraries from requirement.txt file:

pip install -r requirements.txt

For all the requirements above:

pip install -r requirements-ci.txt
pip install -r requirements-optional.txt
pip install -r requirements-tf.txt
pip install -r requirements.txt

Citing

@misc{maml,
    author = {Chen, Chi and Zuo, Yunxing and Ye, Weike and Ong, Shyue Ping},
    title = {{Maml - materials machine learning package}},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {https://github.com/materialsvirtuallab/maml},
}

For the ML-IAP package (maml.pes), please cite:

Zuo, Y.; Chen, C.; Li, X.; Deng, Z.; Chen, Y.; Behler, J.; Csányi, G.; Shapeev, A. V.; Thompson, A. P.;
Wood, M. A.; Ong, S. P. Performance and Cost Assessment of Machine Learning Interatomic Potentials.
J. Phys. Chem. A 2020, 124 (4), 731–745. https://doi.org/10.1021/acs.jpca.9b08723.