maml
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
Science Score: 49.0%
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 6 DOI reference(s) in README -
○Academic publication links
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✓Committers with academic emails
11 of 33 committers (33.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (16.6%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.
Basic Info
Statistics
- Stars: 425
- Watchers: 19
- Forks: 89
- Open Issues: 11
- Releases: 15
Topics
Metadata Files
README.md
maml (MAterials Machine Learning) is a Python package that aims to provide useful high-level interfaces that make ML for materials science as easy as possible.
The goal of maml is not to duplicate functionality already available in other packages. maml relies on well-established packages such as scikit-learn and tensorflow for implementations of ML algorithms, as well as other materials science packages such as pymatgen and matminer for crystal/molecule manipulation and feature generation.
Official documentation at https://materialsvirtuallab.github.io/maml/
Features
- 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.
a) Bispectrum coefficients b) Behler Parrinello symmetry functions c) Smooth Overlap of Atom Position (SOAP) d) Graph network features (composition, site and structure)
Use ML to learn relationship between features and targets. Currently, the
mamlsupportssklearnandkerasmodels.Applications:
a) pes for modelling the potential energy surface, constructing surrogate models for property prediction.
i) Neural Network Potential (NNP) ii) Gaussian approximation potential (GAP) with SOAP features iii) Spectral neighbor analysis potential (SNAP) iv) Moment Tensor Potential (MTP)
b) rfxas for random forest models in predicting atomic local environments from X-ray absorption spectroscopy.
c) bowsr for rapid structural relaxation with bayesian optimization and surrogate energy model.
Installation
Pip install via PyPI:
bash
pip install maml
To run the potential energy surface (pes), lammps installation is required you can install from source or from conda::
bash
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 requirements.txt file::
bash
pip install -r requirements.txt
For all the requirements above:
bash
pip install -r requirements-ci.txt
pip install -r requirements-optional.txt
pip install -r requirements-dl.txt
pip install -r requirements.txt
Usage
Many Jupyter notebooks are available on usage. See notebooks. We also have a tool and tutorial lecture at nanoHUB.
API documentation
See API docs.
Citing
txt
@misc{
maml,
author = {Chen, Chi and Zuo, Yunxing, Ye, Weike, Ji, Qi and Ong, Shyue Ping},
title = {{Maml - materials machine learning package}},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/materialsvirtuallab/maml}},
}
For the ML-IAP package (maml.pes), please cite::
txt
Zuo, Y.; Chen, C.; Li, X.; Deng, Z.; Chen, Y.; Behler, J.; Csnyi, 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), 731745. https://doi.org/10.1021/acs.jpca.9b08723.
For the BOWSR package (maml.bowsr), please cite::
txt
Zuo, Y.; Qin, M.; Chen, C.; Ye, W.; Li, X.; Luo, J.; Ong, S. P. Accelerating Materials Discovery with Bayesian
Optimization and Graph Deep Learning. Materials Today 2021, 51, 126135.
https://doi.org/10.1016/j.mattod.2021.08.012.
For the AtomSets model (maml.models.AtomSets), please cite::
txt
Chen, C.; Ong, S. P. AtomSets as a hierarchical transfer learning framework for small and large materials
datasets. Npj Comput. Mater. 2021, 7, 173. https://doi.org/10.1038/s41524-021-00639-w
Owner
- Name: Materials Virtual Lab
- Login: materialsvirtuallab
- Kind: organization
- Email: ongsp@ucsd.edu
- Location: La Jolla, CA
- Website: www.materialsvirtuallab.org
- Repositories: 33
- Profile: https://github.com/materialsvirtuallab
The Materials Virtual Lab is dedicated to the application of first principles calculations and informatics to accelerate materials design.
GitHub Events
Total
- Create event: 22
- Release event: 2
- Issues event: 6
- Watch event: 59
- Delete event: 26
- Member event: 1
- Issue comment event: 46
- Push event: 65
- Pull request review event: 5
- Pull request review comment event: 3
- Pull request event: 56
- Fork event: 12
Last Year
- Create event: 22
- Release event: 2
- Issues event: 6
- Watch event: 59
- Delete event: 26
- Member event: 1
- Issue comment event: 46
- Push event: 65
- Pull request review event: 5
- Pull request review comment event: 3
- Pull request event: 56
- Fork event: 12
Committers
Last synced: 12 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Chi Chen | c****3@e****u | 337 |
| Shyue Ping Ong | s****p@g****m | 202 |
| dependabot-preview[bot] | 2****] | 192 |
| dependabot[bot] | 4****] | 159 |
| YunxingZuo | z****g@m****n | 89 |
| Shyue Ping Ong | sp@o****i | 70 |
| w6ye | w****e@u****u | 46 |
| pre-commit-ci[bot] | 6****] | 42 |
| JiQi535 | j****i@e****u | 34 |
| Ji Qi | q****i@j****m | 17 |
| Zishen Wang | 6****z | 12 |
| Ji Qi | q****i@b****u | 9 |
| Ji Qi | q****i@J****l | 8 |
| Ji Qi | q****i@J****l | 6 |
| Janosh Riebesell | j****l@g****m | 6 |
| Ji Qi | q****i@J****n | 5 |
| kenko911 | k****1@g****m | 4 |
| Ji Qi | q****i@b****u | 3 |
| Chi Chen | c****3@u****u | 2 |
| Hui Zheng | h****1@e****u | 2 |
| mausam1112 | 4****2 | 2 |
| Ji Qi | q****i@J****l | 1 |
| JJ | jj@t****m | 1 |
| Ji Qi | q****i@J****l | 1 |
| Ji Qi | j****i@l****u | 1 |
| Hexuan Peng | h****5@c****u | 1 |
| JiQi535 | 5****5 | 1 |
| Matthew Evans | 7****s | 1 |
| Rhys Goodall | r****l@o****m | 1 |
| Sean Kavanagh | s****9@i****k | 1 |
| and 3 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 28
- Total pull requests: 319
- Average time to close issues: 3 months
- Average time to close pull requests: 27 days
- Total issue authors: 24
- Total pull request authors: 13
- Average comments per issue: 2.71
- Average comments per pull request: 0.96
- Merged pull requests: 192
- Bot issues: 1
- Bot pull requests: 270
Past Year
- Issues: 4
- Pull requests: 67
- Average time to close issues: 8 days
- Average time to close pull requests: about 2 months
- Issue authors: 4
- Pull request authors: 8
- Average comments per issue: 0.5
- Average comments per pull request: 1.19
- Merged pull requests: 26
- Bot issues: 0
- Bot pull requests: 50
Top Authors
Issue Authors
- janosh (4)
- michaelmacisaac (2)
- dependabot[bot] (2)
- loilisxka (1)
- tomblister (1)
- QuantumMisaka (1)
- zhenming-xu (1)
- Mu-Jinming (1)
- Guancred (1)
- Andrew-S-Rosen (1)
- aishwaryo (1)
- shyuep (1)
- mind-dime (1)
- kavanase (1)
- wilsonnieto (1)
Pull Request Authors
- dependabot[bot] (258)
- pre-commit-ci[bot] (29)
- JiQi535 (19)
- zz11ss11zz (8)
- kenko911 (6)
- drakeyu (4)
- Andrew-S-Rosen (4)
- janosh (3)
- dsun980701 (2)
- computerscienceiscool (2)
- kavanase (2)
- ml-evs (1)
- Tinaatucsd (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 3
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Total downloads:
- pypi 418 last-month
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Total dependent packages: 1
(may contain duplicates) -
Total dependent repositories: 1
(may contain duplicates) - Total versions: 42
- Total maintainers: 2
proxy.golang.org: github.com/materialsvirtuallab/maml
- Documentation: https://pkg.go.dev/github.com/materialsvirtuallab/maml#section-documentation
- License: bsd-3-clause
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Latest release: v2025.4.1+incompatible
published 11 months ago
Rankings
pypi.org: maml
MAterials Machine Learning (maml) is a machine learning library for materials science.
- Documentation: https://maml.readthedocs.io/
- License: BSD-3-Clause
-
Latest release: 2025.4.3
published 11 months ago
Rankings
Maintainers (2)
conda-forge.org: maml
maml (MAterials Machine Learning) is a Python package that aims to provide useful high-level interfaces that make ML for materials science as easy as possible. The goal of maml is not to duplicate functionality already available in other packages. maml relies on well-established packages such as scikit-learn and tensorflow for implementations of ML algorithms, as well as other materials science packages such as pymatgen and matminer for crystal/ molecule manipulation and feature generation.
- Homepage: https://materialsvirtuallab.github.io/maml
- License: BSD-3-Clause
-
Latest release: 2022.9.20
published over 3 years ago
Rankings
Dependencies
- actions/checkout v2 composite
- actions/setup-python v2 composite
- actions/cache v2 composite
- actions/checkout v2 composite
- actions/setup-python v2 composite
- AndreMiras/coveralls-python-action v20201129 composite
- actions/cache v2 composite
- actions/checkout v2 composite
- actions/setup-python v2 composite