Automatic material discovery with desired properties is a fundamental challenge for material sciences. Considerable attention has recently been devoted to generating stable crystal structures. While existing work has shown impressive success on supervised tasks such as property prediction, the progress on unsupervised tasks such as material generation is still hampered by the limited extent to which the equivalent geometric representations of the same crystal are considered. To address this challenge, we propose EMPNN a periodic equivariant message-passing neural network that learns crystal lattice deformation in an unsupervised fashion. Our model equivalently acts on lattice according to the deformation action that must be performed, making it suitable for crystal generation, relaxation and optimisation. We present experimental evaluations that demonstrate the effectiveness of our approach.

翻译：自动发现具有所需性质的材料是材料科学的一个基本挑战。近年来，生成稳定晶体结构的研究备受关注。尽管现有工作在性质预测等监督任务上取得了显著成功，但在材料生成等无监督任务上的进展仍受到对同一晶体等价几何表示考虑有限的制约。为应对这一挑战，我们提出了EMPNN，一种以无监督方式学习晶格变形的周期等变消息传递神经网络。我们的模型根据需执行的变形操作对晶格进行等变作用，使其适用于晶体生成、弛豫与优化。实验结果展示了我们方法的有效性。