大规模量子修正核磁共振晶体学
Quantum-corrected NMR crystallography at scale
AI总结：
本研究提出量子核修正方法，通过机器学习模型提升氢键质子的化学位移预测精度，无需经验修正，并扩展至非晶材料的模拟。
摘要：
通过化学移驱动的核磁共振晶体学确定结构，依赖于将固态核磁共振实验中测得的化学位移与模拟进行比较。然而，计算成本限制了化学位移预测的准确性，通常依赖于低级电子结构近似，并忽略热力学和量子力学的核运动，导致较大的误差，尤其是对于高度信息性的氢键质子。为了解决这些限制，我们引入了一种量子核修正（QNC-NMR）方法。我们使用PET-MOLS，一种新的机器学习模型，该模型可以跨分子晶体转移互原子势能，生成廉价的量子族。将它们作为输入传递给化学位移模型，可以实现对氢键质子与实验一致性的两倍改进，而无需经验修正。能够采样与实验条件一致的结构，使通过微调屏蔽模型进一步改进屏蔽模型成为可能。有利的系统规模扩展能力使得类似改进也适用于其他无法进行显式DFT模拟的非晶材料。
英文摘要：
Structure determination by chemical-shift-driven NMR crystallography relies on comparing chemical shieldings measured in solid-state NMR experiments with simulations. However, computational cost limits the accuracy of shielding predictions, that usually rely on low-level electronic-structure approximations and neglect thermal and quantum mechanical nuclear motion, leading to large errors, especially for highly informative hydrogen-bonded protons. To address these limitations, we introduce a quantum-nuclei-corrected (QNC-NMR) approach. We generate inexpensively quantum ensembles using PET-MOLS, a novel machine-learning learning model of the interatomic potential transferable across molecular crystals. Using them as inputs to a chemical-shift model results in a 33 % improvement of the agreement with experiments for hydrogen-bonded protons, without the need for empirical corrections. The ability to sample structures consistent with the experimental conditions enables further refinement of the shielding model by finetuning it against measured shieldings. The favorable scaling with system size allows similar improvements for amorphous materials that are otherwise inaccessible to explicit DFT simulations.
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