https://github.com/sparks-baird/mat_discover

A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
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DOI references
Found 15 DOI reference(s) in README
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Academic publication links
Links to: zenodo.org
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Committers with academic emails
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (11.1%) to scientific vocabulary

Keywords

adaptive-design bayesian-optimization crabnet earth-mover-distance earth-movers-distance machine-learning matdiscover materials-discoveries materials-discovery materials-informatics materials-project materials-science materials-screening numba predict-materials-properties python pytorch wasserstein wasserstein-distance wasserstein-metric

Keywords from Contributors

generative interactive projection sequences genomics observability autograding hacking shellcodes archival

Last synced: 5 months ago · JSON representation

Repository

A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.

Basic Info

Host: GitHub
Owner: sparks-baird
License: mit
Language: Python
Default Branch: main
Homepage: https://mat-discover.readthedocs.io/
Size: 88.7 MB

Statistics

Stars: 44
Watchers: 1
Forks: 9
Open Issues: 29
Releases: 24

Topics

Created over 4 years ago · Last pushed over 1 year ago

Metadata Files

Readme Contributing License

README.md

DiSCoVeR

Examples, Paper, and Docs

A materials discovery algorithm geared towards exploring high performance candidates in new chemical spaces using composition-only.

^{Bulk modulus values overlaid on DensMAP densities (cropped).}

The documentation describes the Descending from Stochastic Clustering Variance Regression (DiSCoVeR) algorithm, how to install mat_discover, and basic usage (fit/predict, custom or built-in datasets, adaptive design, and cluster plots). Interactive plots for several types of Pareto front plots are available. We also describe how to contribute, and what to do if you run into bugs or have questions. Various examples (including a teaching example), the interactive figures mentioned, and the Python API are also hosted at https://mat-discover.readthedocs.io. The open-access article is published at Digital Discovery. If you find this useful, please consider citing as follows:

Citing

Baird, S. G.; Diep, T. Q.; Sparks, T. D. DiSCoVeR: A Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions. Digital Discovery 2022. https://doi.org/10.1039/D1DD00028D.

bib @article{bairdDiSCoVeRMaterialsDiscovery2022, title = {{{DiSCoVeR}}: A {{Materials Discovery Screening Tool}} for {{High Performance}}, {{Unique Chemical Compositions}}}, shorttitle = {{{DiSCoVeR}}}, author = {Baird, Sterling Gregory and Diep, Tran Q. and Sparks, Taylor D.}, year = {2022}, month = feb, journal = {Digital Discovery}, publisher = {{RSC}}, issn = {2635-098X}, doi = {10.1039/D1DD00028D}, abstract = {We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR) (https://github.com/sparks-baird/mat_discover), a Python tool for identifying and assessing high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model. In this work, we create pairwise distance matrices between compounds via Element Mover's Distance (ElMD) and use these to create 2D density-aware embeddings for chemical compositions via Density-preserving Uniform Manifold Approximation and Projection (DensMAP). Because ElMD assigns distances between compounds that are more chemically intuitive than Euclidean-based distances, the compounds can then be clustered into chemically homogeneous clusters via Hierarchical Density-based Spatial Clustering of Applications with Noise (HDBSCAN*). In combination with performance predictions via Compositionally-Restricted Attention-Based Network (CrabNet), we introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multi-objective Pareto front plots and assign a weighted score to each composition that encompasses the trade-off between performance and density-based chemical uniqueness. In addition to density-based metrics, we explore an additional uniqueness proxy related to property gradients in DensMAP space. As a validation study, we use DiSCoVeR to screen materials for both performance and uniqueness to extrapolate to new chemical spaces. Top-10 rankings are provided for the compound-wise density and property gradient uniqueness proxies. Top-ranked compounds can be further curated via literature searches, physics-based simulations, and/or experimental synthesis. Finally, we compare DiSCoVeR against the naive baseline of random search for several parameter combinations in an adaptive design scheme. To our knowledge, this is the first time automated screening has been performed with explicit emphasis on discovering high-performing, novel materials.}, langid = {english}, }

If you use this software, in addition to the above reference, please also cite the Zenodo DOI and state the version that you used:

Sterling Baird. (2022). sparks-baird/mat_discover. Zenodo. https://doi.org/10.5281/zenodo.5594678

bib @software{sterling_baird_2022_6116258, author = {Sterling Baird}, title = {sparks-baird/mat\_discover}, month = feb, year = 2022, publisher = {Zenodo}, doi = {10.5281/zenodo.5594678}, url = {https://doi.org/10.5281/zenodo.5594678} }

If you use this software as an installed dependency in another GitHub repository, please add mat_discover to a requirements.txt file in your repository via e.g.: bash pip install pipreqs pipreqs . pipreqs generates (at least a starting point) for a requirements.txt file based on import statements in your working directory and subfolders. For an example, see requirements.txt.

Owner

Name: Sparks/Baird Materials Informatics
Login: sparks-baird
Kind: organization
Email: sterling.baird@utah.edu
Location: United States of America

Repositories: 63
Profile: https://github.com/sparks-baird

Sterling Baird and Taylor Sparks Materials Informatics Projects

GitHub Events

Total

Watch event: 5

Last Year

Watch event: 5

Committers

Last synced: 10 months ago

All Time

Total Commits: 1,367
Total Committers: 5
Avg Commits per committer: 273.4
Development Distribution Score (DDS): 0.109

Past Year

Commits: 1
Committers: 1
Avg Commits per committer: 1.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
sgbaird	s**d@i**m	1,218
SurgeArrester	c**h@r**k	76
dependabot[bot]	4****]	68
sgbaird-alt	7****t	4
Hasan Sayeed	h**3@g**m	1

Committer Domains (Top 20 + Academic)

rgreaves.me.uk: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 31
Total pull requests: 90
Average time to close issues: 21 days
Average time to close pull requests: 14 days
Total issue authors: 5
Total pull request authors: 3
Average comments per issue: 1.81
Average comments per pull request: 0.36
Merged pull requests: 60
Bot issues: 0
Bot pull requests: 79

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

sgbaird (24)
ancarnevali (3)
Asif-Iqbal-Bhatti (1)
cseeg (1)
hasan-sayeed (1)

Pull Request Authors

dependabot[bot] (77)
sgbaird (10)
hasan-sayeed (1)

Top Labels

Issue Labels

bug (19) enhancement (11) google colab (2) good first issue (1) documentation (1)

Pull Request Labels

dependencies (77)

Packages

Total packages: 1
Total downloads:
- pypi 140 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 33
Total maintainers: 1

pypi.org: mat_discover

Data-driven materials discovery based on composition or structure.

Documentation: https://mat_discover.readthedocs.io/
License: MIT License
Latest release: 2.2.9
published over 2 years ago

Versions: 33
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 140 Last month

Rankings

Dependent packages count: 6.6%

Stargazers count: 11.5%

Forks count: 12.3%

Average: 16.2%

Downloads: 19.7%

Dependent repos count: 30.6%

Maintainers (1)

sgbaird

Last synced: 6 months ago

Dependencies

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environment.yml pypi

ElM2D ==0.4.1
matbench *
matbench_genmetrics *

pyproject.toml pypi

chem_wasserstein >=1.0.8,<2.0.0
composition_based_feature_vector *
crabnet >=2.0.5,<3.0.0
cython *
dill *
gridrdf ==0.3.0
hdbscan *
joblib *
kaleido *
m3gnet *
matbench *
matbench_genmetrics >= 0.6.1
matplotlib >=3.4.3
megnet *
numba >=0.53.1
pandas *
plotly >=5.6.0
pqdm *
scikit_learn *
scipy *
seaborn *
torch <2
tqdm *
umap-learn *

requirements.txt pypi

chem_wasserstein ==1.0.9
composition-based-feature-vector ==1.0.4
conda-souschef ==2.2.3
crabnet ==2.0.2
dill ==0.3.6
hdbscan ==0.8.28
joblib ==1.1.
matbench_genmetrics ==0.6.0
matplotlib ==3.6.2
myst-parser ==0.18.1
pandas ==1.5.2
plotly ==5.11.0
pqdm ==0.2.0
scikit-learn ==1.2.0
scipy ==1.9.3
sphinx ==5.3.0
sphinx_copybutton ==0.5.1
sphinx_rtd_theme ==1.1.1
umap_learn ==0.5.3

setup.py pypi