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.

翻译：准确预测晶体固体的弹性性质对于计算材料科学至关重要。然而，传统的原子尺度从头算方法计算量巨大，尤其是在研究单胞内原子数较多的复杂材料时。我们提出了一种新颖的数据驱动方法，利用SE(3)等变图神经网络高效预测晶体结构的弹性性质。该方法能够以与近期数据驱动研究相当的精度，获得重要的标量弹性模量。更重要的是，这类具有对称感知能力的图神经网络模型还能预测应变能密度及相关弹性常数，这是受材料晶体群显著影响的基本张量量。该模型能根据晶体结构的对称性，一致地区分应变能密度张量的独立分量。最后，我们的深度学习模型具有有意义的潜在特征，可对弹性性质进行可解释性预测。