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.

翻译：核磁共振（NMR）是一种对物质的局部原子结构敏感的强大光谱技术。NMR参数的计算预测有助于解释实验数据和验证结构模型，而机器学习（ML）已成为实现此类预测的有效途径。本文系统研究了图神经网络方法在表示和学习固态NMR张量量——特别是各向异性磁屏蔽和电场梯度——方面的应用。我们评估了不同ML模型的数值精度如何转化为对实验相关NMR特性（化学位移、四极耦合常数、张量取向，甚至静态一维光谱）的预测质量。我们将这些ML模型应用于结构多样的非晶态SiO$_2$构型数据集（涵盖广泛的密度和局部有序度范围）、超越传统第一性原理方法处理能力的更大构型，以及方石英中$\alpha\unicode{x2013}\beta$反转的动力学过程。我们的工作标志着在简化ML驱动的复杂材料静态与动态行为NMR预测，以及弥合第一性原理建模与真实世界实验数据之间差距方面迈出了一步。