用笛卡尔局部环境张量高效预测晶体高阶张量性质
Efficient Equivariant High-Order Crystal Tensor Prediction via Cartesian Local-Environment Many-Body Coupling
- 通过可学习的通道空间交互实现多体耦合
- 在多种高阶张量预测任务中精度超越现有方法
- 适合需要高效高阶张量建模的研究者
从原子结构端到端预测高阶晶体张量性质仍具挑战：虽然球谐等变模型表达能力强，但其克莱布施-戈登张量积对高阶目标带来巨大计算与内存开销。本文提出笛卡尔环境相互作用张量网络（CEITNet），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。通过在通道空间中学习并使用笛卡尔张量基构造等变输出，CEITNet实现了高阶张量的高效构建。在介电（二阶）、压电（三阶）和弹性（四阶）张量预测基准数据集上，该方法在关键精度指标上超越先前高阶预测方法，同时具备高计算效率。
原文摘要 · Abstract (English)
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.
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