StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks

TL;DR

This paper presents StrainTensorNet, a symmetry-aware SE(3)-equivariant graph neural network that efficiently predicts elastic properties and tensorial quantities of crystalline solids with accuracy comparable to existing data-driven methods.

Contribution

It introduces a novel SE(3)-equivariant GNN that models elastic properties and strain energy tensors, incorporating crystal symmetry for improved interpretability and accuracy.

Findings

Accurately predicts elastic moduli of crystalline solids.

Models strain energy density and elastic tensors respecting crystal symmetry.

Provides interpretable latent features for elastic property prediction.

Abstract

Accurately predicting the elastic properties of crystalline solids is vital for computational materials science. However, traditional atomistic scale ab initio approaches are computationally intensive, especially for studying complex materials with a large number of atoms in a unit cell. We introduce a novel data-driven approach to efficiently predict the elastic properties of crystal structures using SE(3)-equivariant graph neural networks (GNNs). This approach yields important scalar elastic moduli with the accuracy comparable to recent data-driven studies. Importantly, our symmetry-aware GNNs model also enables the prediction of the strain energy density (SED) and the associated elastic constants, the fundamental tensorial quantities that are significantly influenced by a material's crystallographic group. The model consistently distinguishes independent elements of SED tensors, in accordance with the symmetry of the crystal structures. Finally, our deep learning model possesses meaningful latent features, offering an interpretable prediction of the elastic properties.