标题： 基于球张量分解的固态核磁共振参数的图神经网络预测 显示英文标题

标题： Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition

摘要： 核磁共振（NMR）是一种对物质局部原子结构敏感的强大光谱技术。计算预测的NMR参数有助于解释实验数据并验证结构模型，而机器学习（ML）已成为进行此类预测的一种高效途径。在这里，我们系统地研究了图神经网络方法在表示和学习固态NMR张量量方面的应用——特别是各向异性磁屏蔽和电场梯度。我们评估了不同ML模型的数值准确性如何转化为实验相关NMR属性（化学位移、四极耦合常数、张量取向以及甚至静态一维光谱）的预测质量。我们将这些ML模型应用于一组结构多样的非晶SiO$_2$配置数据集，该数据集涵盖了从低密度到高局部有序性范围，并扩展到了传统第一性原理方法无法触及的更大配置，以及方石英中$\alpha\unicode{x2013}\beta$翻转的动力学。我们的工作标志着朝着通过机器学习推动复杂材料静态和动态行为的NMR预测迈出了一步，并朝着弥合第一性原理建模与现实世界实验数据之间的差距迈进了一步。 显示英文摘要

摘要： Nuclear magnetic resonance (NMR) is a powerful spectroscopic technique that is sensitive to the local atomic structure of matter. Computational predictions of NMR parameters can help to interpret experimental data and validate structural models, and machine learning (ML) has emerged as an efficient route to making such predictions. Here, we systematically study graph-neural-network approaches to representing and learning tensor quantities for solid-state NMR -- specifically, the anisotropic magnetic shielding and the electric field gradient. We assess how the numerical accuracy of different ML models translates into prediction quality for experimentally relevant NMR properties: chemical shifts, quadrupolar coupling constants, tensor orientations, and even static 1D spectra. We apply these ML models to a structurally diverse dataset of amorphous SiO$_2$ configurations, spanning a wide range of density and local order, to larger configurations beyond the reach of traditional first-principles methods, and to the dynamics of the $\alpha\unicode{x2013}\beta$ inversion in cristobalite. Our work marks a step toward streamlining ML-driven NMR predictions for both static and dynamic behavior of complex materials, and toward bridging the gap between first-principles modeling and real-world experimental data.