End-to-end prediction of high-order crystal tensor properties from atomic structures remains challenging: while spherical-harmonic equivariant models are expressive, their Clebsch-Gordan tensor products incur substantial compute and memory costs for higher-order targets. We propose the Cartesian Environment Interaction Tensor Network (CEITNet), an approach that constructs a multi-channel Cartesian local environment tensor for each atom and performs flexible many-body mixing via a learnable channel-space interaction. By performing learning in channel space and using Cartesian tensor bases to assemble equivariant outputs, CEITNet enables efficient construction of high-order tensor. Across benchmark datasets for order-2 dielectric, order-3 piezoelectric, and order-4 elastic tensor prediction, CEITNet surpasses prior high-order prediction methods on key accuracy criteria while offering high computational efficiency.

翻译：从原子结构端到端预测高阶晶体张量性质仍具挑战性：虽然球谐等变模型具有强表达能力，但其Clebsch-Gordan张量积在计算高阶目标时会产生巨大的计算与内存开销。我们提出笛卡尔环境交互张量网络（CEITNet），该方法为每个原子构建多通道笛卡尔局部环境张量，并通过可学习的通道空间交互实现灵活的多体混合。通过在通道空间进行学习，并利用笛卡尔张量基组装等变输出，CEITNet能够高效构建高阶张量。在二阶介电张量、三阶压电张量与四阶弹性张量预测的基准数据集测试中，CEITNet在关键精度指标上超越了现有高阶预测方法，同时具备较高的计算效率。