license: cc-by-4.0
configs:
- config_name: default
data_files:
- split: train
path: train.parquet
- split: val
path: val.parquet
- split: test
path: test.parquet
Alex-MP-20
This is a version of the Alex-MP-20 dataset with custom training/validation/testing splits created for the Open Materials Generation (OMatG) crystal structure prediction model.
The original dataset was created as a training dataset for the MatterGen materials design model. The original citation information is provided below.
Alex-MP-20 was published under the MIT license by:
Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka, A generative model for inorganic materials design, Nature (2025), 10.1038/s41586-025-08628-5
This dataset contains structures from the Alexandria (Schmidt et al. 2022) and MP-20 datasets. For details on MP-20, see here.
The Alexandria dataset was published under Creative Commons Attribution 4.0 International by:
Jonathan Schmidt, Noah Hoffmann, Hai-Chen Wang, Pedro Borlido, Pedro J. M.A. Carriço, Tiago F. T. Cerqueira, Silvana Botti, Miguel A. L. Marques, Large-scale machine-learning-assisted exploration of the whole materials space, Materials Cloud Archive 2022.126 (2022), https://doi.org/10.24435/materialscloud:m7-50
Additional information pertaining to the creation of Alex-MP-20 can be found here
Open Materials Generation (OMatG)
About
OMatG is a generative model for crystal structure prediction and de novo generation of inorganic crystals.
This repository hosts our model checkpoints and benchmark datasets.
Citation
Please cite our paper on OpenReview if using our models or datasets.
Links
OMatG on GitHub: See this repository for installation, training and usage instructions.
KIM Initiative: Knowledgebase of Interatomic Models. Tools and resources for researchers in materials science and chemistry.
Fermat-ML on GitHub: Foundational Representation of Materials. Machine learning foundation model for materials and chemistry discovery.