A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction
This work addresses the challenge of predicting tensor properties for materials science, with incremental improvements in enforcing symmetry constraints.
The paper tackles the prediction of tensor properties of crystalline materials, such as dielectric and elastic tensors, by proposing GMTNet, which achieves promising performance and ensures predictions are consistent with required crystal symmetries.
We consider the prediction of general tensor properties of crystalline materials, including dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the predictions satisfy the unique tensor equivariance to O(3) group and invariance to crystal space groups. To this end, we propose a General Materials Tensor Network (GMTNet), which is carefully designed to satisfy the required symmetries. To evaluate our method, we curate a dataset and establish evaluation metrics that are tailored to the intricacies of crystal tensor predictions. Experimental results show that our GMTNet not only achieves promising performance on crystal tensors of various orders but also generates predictions fully consistent with the intrinsic crystal symmetries. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).