Dataset | Models | Colabs | License | Disclaimer | Upcoming | Citing
From microchips to batteries and photovoltaics, discovery of inorganic crystals is a fundamental problem in materials science. Graph Networks for Materials Science (GNoME) is a project centered around scaling machine learning methods to tackle this core task. With results recently published, this repository serves to share the discovery of 381,000 novel stable materials with the wider materials science community and hopefully enable exciting new research via the updated convex hull.
This is a research project, not an official Google product. Expect bugs as the repository expands and sharp edges. Please help by exploring the structures and let us know what you think!
The dataset described in the original paper is provided across multiple file formats. For more details, including how to download the dataset, please see our dataset descriptor file in DATASET.md.
Summarized A summary of the dataset is provided in CSV format. This file contains compositions and raw energies from Density Functional Theory (DFT) calculations, as well as other popular measurements (e.g. formation energy and decomposition energy).
Structure Loading of structures is slightly more cumbersome due to file sizes involved. Due to the organization of the convex hull, only one structure is needed per composition, so results from the summary can be used to pull from the compressed data directory available in the linked Cloud Bucket.
r²SCAN Baseline calculations were performed via PBE functional for the calculations. The paper also reports metrics for binaries and tenaries with the r²SCAN functional. A summary of calculated energies and associated metrics is included for these calculations.
We provide model definitions for the two sets of models used in the paper.
GNoME were the predominant model behind new materials discovery. This simple message passing architecture was optimized by training on a snapshot of Materials Project from 2018, leading to state-of-the-art results of 21meV/atom.
Nequip corresponds to the architecture created by Batzner et al. (2022). This architecture was used to train the interatomic potentials described in the paper to learn the dynamics from the large dataset. We provide an implementation in JAX as well as basic configuration parameters for the corresponding architecture.
Colab examples of how to interact with the dataset and models will be released to provide an easier interface with both.
The Colab notebooks and associated code provided in this repository are licensed under the Apache License, Version 2.0. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0.
Data contained in the Graph Networks for Materials Exploration Database is available for use under the terms of the Creative Commons Attribution Noncommercial 4.0 International Licence (CC BY NC 4.0). You may obtain a copy of the License at https://creativecommons.org/licenses/by-nc/4.0/. The dataset was created using the Vienna Ab initio Simulation Package (VASP) in order to run calculations from Density Functional Theory.
- Repeated calculations of structures from the Materials Project and other agglomerated datasets (Open Quantum Materials Database, WBM) under consistent settings (defining the complete convex hull)
- Example colabs of loading materials from the CSVs and calculating convex hull energies
- Code to visualize structures in a colab notebooks
- Reference structures and search paths
- Model training colabs and configs
- Additional material properties (e.g. electronic band structure)
This is not an official Google product.
Graph Networks for Materials Exploration Database, Copyright, Google LLC, (2023).
Data in the Graph Networks for Materials Exploration Database is for theoretical modeling only, caution should be exercised in its use. The Graph Networks for Materials Exploration Database is not intended for, and is not approved for, any medical or clinical use. The Graph Networks for Materials Exploration Database is experimental in nature and provided on an “as is” basis. To the maximum extent permitted at law, Google disclaims all representations, conditions and warranties, whether express or implied, in relation to the Graph Networks for Materials Exploration Database (including without limitation for non-infringement of third party intellectual property rights, satisfactory quality, merchantability or fitness for a particular purpose), and the user shall hold Google free and harmless in connection with their use of such content.
If you are using this resource please cite our paper
@article{merchant2023scaling,
title={Scaling deep learning for materials discovery},
author={Amil Merchant and Simon Batzner and Samuel S. Schoenholz and Muratahan Aykol and Gowoon Cheon and Ekin Dogus Cubuk},
journal={Nature},
year={2023},
doi={10.1038/s41586-023-06735-9},
href={https://www.nature.com/articles/s41586-023-06735-9},
}