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A General Tensor Prediction Framework Based on Graph Neural Networks.

Yang Zhong^1,2, Hongyu Yu^1,2, Xingao Gong^1,2,3

¹Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan University, Shanghai 200433, China.

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Summary

This summary is machine-generated.

This study introduces a new graph neural network (GNN) framework for predicting directional molecular properties. The edge-based tensor prediction GNN accurately handles directional data, expanding GNN applications.

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Area of Science:

Computational chemistry
Materials science
Machine learning

Background:

Graph neural networks (GNNs) excel at predicting invariant molecular properties.
Traditional GNNs cannot predict directional properties due to limitations with rotational equivariance.
This restricts GNN applications to scalar property predictions.

Purpose of the Study:

To develop a general framework for GNNs capable of predicting directional properties.
To enable GNNs to handle tensor properties beyond invariant scalars.
To expand the applicability of GNNs in materials and molecular science.

Main Methods:

Proposed an edge-based tensor prediction graph neural network framework.
Represented tensors as linear combinations of local spatial components projected onto edge directions.
Ensured rotational equivariance and symmetry satisfaction within the local structure.

Main Results:

Successfully predicted various tensor properties from first to third order.
Demonstrated the accuracy and universality of the proposed framework.
Validated the framework's ability to handle directional properties.

Conclusions:

The developed framework overcomes limitations of traditional GNNs for directional properties.
This advancement enables GNNs to predict a broader range of physical properties.
The framework opens new avenues for GNN applications in computational science.