Model Overview

A Graph Neural Network using a variant of self-attention for 3D points and graphs processing.

Model Architecture

The model consists of stacked layers of equivariant graph self-attention and equivariant normalization. Lastly, a Tensor Field Network convolution is applied to obtain invariant features. Graph pooling (mean or max over the nodes) is applied to these features, and the result is fed to a final MLP to get scalar predictions.

In this setup, the model is a graph-to-scalar network. The pooling can be removed to obtain a graph-to-graph network, and the final TFN can be modified to output features of any type (invariant scalars, 3D vectors, ...).

Training

This model was trained using script available on NGC and in GitHub repo

Dataset

The following datasets were used to train this model:

• Quantum Machines 9 - Database providing quantum chemical properties for a relevant, consistent, and comprehensive chemical space of small organic molecules.

Performance

Performance numbers for this model are available in NGC

References

• original paper
• NVIDIA model implementation in NGC
• NVIDIA model implementation on GitHub

License

This model was trained using open-source software available in Deep Learning Examples repository. For terms of use, please refer to the license of the script and the datasets the model was derived from.