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Accurately predicting the elastic properties of crystalline solids is vitalfor computational materials science. However, traditional atomistic scale abinitio approaches are computationally intensive, especially for studyingcomplex materials with a large number of atoms in a unit cell. We introduce anovel data-driven approach to efficiently predict the elastic properties ofcrystal structures using SE(3)-equivariant graph neural networks (GNNs). Thisapproach yields important scalar elastic moduli with the accuracy comparable torecent data-driven studies. Importantly, our symmetry-aware GNNs model alsoenables the prediction of the strain energy density (SED) and the associatedelastic constants, the fundamental tensorial quantities that are significantlyinfluenced by a material's crystallographic group. The model consistentlydistinguishes independent elements of SED tensors, in accordance with thesymmetry of the crystal structures. Finally, our deep learning model possessesmeaningful latent features, offering an interpretable prediction of the elasticproperties.

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