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Sponsored by
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ibm-research / materials.pos-egnn
Model's Last Updated: August 07 2025
graph-ml
Introduction of materials.pos-egnn
Model Details of materials.pos-egnn
Position-based Equivariant Graph Neural Network (
pos-egnn
)
This repository contains PyTorch source code for loading and performing inference using the
pos-egnn
, a foundation model for Chemistry and Materials.
GitHub : https://github.com/IBM/materials/tree/main/models/pos_egnn
HuggingFace : https://huggingface.co/ibm-research/materials.pos-egnn
Introduction
We present
pos-egnn
, a Position-based Equivariant Graph Neural Network foundation model for Chemistry and Materials. The model was pre-trained on 1.4M samples (i.e., 90%) from the Materials Project Trajectory (MPtrj) dataset to predict energies, forces and stress.
pos-egnn
can be used as a machine-learning potential, as a feature extractor, or can be fine-tuned for specific downstream tasks.
Besides the model weigths
pos-egnn.v1-6M.pt
(download from
HuggingFace
), we also provide an
example.ipynb
notebook (download from
GitHub
), which demonstrates how to perform inference, feature extraction and molecular dynamics simulation with the model.
For more information, please reach out to [email protected] and/or [email protected]
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Url of materials.pos-egnn
materials.pos-egnn huggingface.co Url
Provider of materials.pos-egnn huggingface.co
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Updated:September 25 2024
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