一个深度学习模型用于分子有机固体中的化学屏蔽,包括 anisotropy
Matthias Kellner1, Jacob B Holmes2, Ruben Rodriguez-Madrid2
1Laboratory of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
The journal of physical chemistry letters
|August 18, 2025
概括
新的深度学习模型ShiftML3显著提高了预测固体中核磁共振 (NMR) 化学屏蔽的准确性. 这一进步使机器学习预测更接近要求初始计算的准确性.
科学领域:
- 固态核磁共振 (NMR) 光谱学 固态核磁共振 (NMR) 光谱学
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
背景情况:
- 核磁共振 (NMR) 化学转移对于确定固体材料中的原子和电子结构至关重要.
- 机器学习 (ML) 模型为预测化学屏蔽提供了对初始计算的有效替代方案.
- 当前的ML模型往往缺乏已建立的DFT方法的预测准确性.
研究的目的:
- 介绍ShiftML3,一种深度学习模型,用于增强对NMR化学屏蔽的预测.
- 提高在分子固体中预测同otropic化学屏蔽和完全屏蔽张量器的准确性和效率.
- 为了减少ML模型和DFT计算之间的预测准确度差距.
主要方法:
- 开发了一个名为ShiftML3.3的深度学习模型.
- 在实验基准数据集上的模型的培训和验证.
- 将ShiftML3的预测准确度与DFT参考计算进行比较.
主要成果:
- ShiftML3在预测H,C和N.15的同位素化学屏蔽方面实现了高准确度.
- ShiftML3的根平均平方误差 (RMSEs) 接近DFT计算的误差:0.53 ppm (H),2.4 ppm (C) 和7.2 ppm (N).
- 该模型成功地预测了全屏蔽张量,而不仅仅是同位素值.
结论:
- ShiftML3代表了基于ML的分子固体NMR屏蔽预测的重大进步.
- 该模型的准确性与DFT方法相美,提供了一个更快,更可扩展的替代方案.
- ShiftML3增强了NMR光谱在固态材料结构阐明方面的实用性.
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