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MPTS-52-CSP
OMatG/MPTS-52-CSP
MPTS-52-CSP is a machine learning model from OMatG. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This is an OMatG (Open Materials Generation) model for crystal structure prediction (CSP) of inorganic crystals trained on the MPTS-52 (Materials Project Time Splits) dataset.
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Updated Dec 10, 2025
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At a glance
- License
- mit
- Access
- Public
- Created
- Jul 16, 2025
- Updated
- Dec 10, 2025
- SHA
- f305d441
Model card
From the Hugging Face model README
Description
This is an OMatG (Open Materials Generation) model for crystal structure prediction (CSP) of inorganic crystals trained on the MPTS-52 (Materials Project Time Splits) dataset.
The subdirectories in this repository contain various model hyperparameters and training checkpoints for a variety of MTPS-52-CSP models.
Uses
The checkpoints and model hyperparameters can be used for prediction of crystalline structures with OMatG, as described in the this README.md file
Recommendations
The Linear-ODE checkpoints currently provide the best results with respect to the match rate.
The VESBD-ODE checkpoints currently provide the best results with respect to average root-mean square distance between generated and matched reference structures.
Citation
Please cite our paper on OpenReview if using OMatG.
Links
OMatG on GitHub: See this repository for OMatG 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.
- License
- mit
- Created
- Jul 16, 2025
- Updated
- Dec 10, 2025