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.
This dataset is also included in OMatG.
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 structure stability, novelty, and uniqueness.
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.