Metadata-Version: 2.1
Name: matbench-discovery
Version: 1.3.0
Summary: A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures
Author-email: Janosh Riebesell <janosh.riebesell@gmail.com>
License: MIT License
        
        Copyright (c) 2022 Janosh Riebesell
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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Project-URL: Homepage, https://janosh.github.io/matbench-discovery
Project-URL: Repo, https://github.com/janosh/matbench-discovery
Project-URL: Package, https://pypi.org/project/matbench-discovery
Keywords: Bayesian optimization,convex hull,high-throughput search,inorganic crystal stability,interatomic potential,machine learning,materials discovery
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: ase>=3.23
Requires-Dist: matplotlib>=3.9.2
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Requires-Dist: wandb>=0.17
Provides-Extra: test
Requires-Dist: pytest-cov>=5; extra == "test"
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Provides-Extra: running-models
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<h1 align="center" style="line-height: 0; margin: 0 auto 1em;">
  <img src="https://github.com/janosh/matbench-discovery/raw/main/site/static/favicon.svg" alt="Logo" width="60px"><br>
  Matbench Discovery
</h1>

<h4 align="center" class="toc-exclude" style="display: none;">

[![arXiv](https://img.shields.io/badge/arXiv-2308.14920-blue?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2308.14920)
[![Tests](https://github.com/janosh/matbench-discovery/actions/workflows/test.yml/badge.svg)](https://github.com/janosh/matbench-discovery/actions/workflows/test.yml)
[![GitHub Pages](https://github.com/janosh/matbench-discovery/actions/workflows/gh-pages.yml/badge.svg)](https://github.com/janosh/matbench-discovery/actions/workflows/gh-pages.yml)
[![Requires Python 3.11+](https://img.shields.io/badge/Python-3.11+-blue.svg?logo=python&logoColor=white)](https://python.org/downloads)
[![PyPI](https://img.shields.io/pypi/v/matbench-discovery?logo=pypi&logoColor=white)](https://pypi.org/project/matbench-discovery?logo=pypi&logoColor=white)

</h4>

> Disclaimer: We evaluate how accurately ML models predict solid-state thermodynamic stability. Although this is an important aspect of high-throughput materials discovery, the ranking cannot give a complete picture of a model's general applicability to materials. A high ranking does not constitute endorsement by the Materials Project.

Matbench Discovery is an [interactive leaderboard](https://janosh.github.io/matbench-discovery/models) and associated [PyPI package](https://pypi.org/project/matbench-discovery) which together make it easy to rank ML energy models on a task designed to simulate a high-throughput discovery campaign for new stable inorganic crystals.

We've tested <slot name="model-count" />models covering multiple methodologies ranging from random forests with structure fingerprints to graph neural networks, from one-shot predictors to iterative Bayesian optimizers and interatomic potential relaxers.

<slot name="best-report" />

Our results show that ML models have become robust enough to deploy them as triaging steps to more effectively allocate compute in high-throughput DFT relaxations. This work provides valuable insights for anyone looking to build large-scale materials databases.

<slot name="metrics-table" />

If you'd like to refer to Matbench Discovery in a publication, please cite the [preprint](https://doi.org/10.48550/arXiv.2308.14920):

> Riebesell, Janosh, Rhys E. A. Goodall, Philipp Benner, Yuan Chiang, Bowen Deng, Alpha A. Lee, Anubhav Jain, and Kristin A. Persson. "Matbench Discovery -- A Framework to Evaluate Machine Learning Crystal Stability Predictions." arXiv, August 28, 2023. https://doi.org/10.48550/arXiv.2308.14920.

We welcome new models additions to the leaderboard through GitHub PRs. See the [contributing guide](https://janosh.github.io/matbench-discovery/contribute) for details.

If you're interested in joining this work, please reach out via [GitHub discussion](https://github.com/janosh/matbench-discovery/discussions) or [email](mailto:janosh.riebesell@gmail.com?subject=Collaborate%20on%20Matbench%20Discovery).

For detailed results and analysis, check out the [preprint](https://janosh.github.io/matbench-discovery/preprint).
