在NMR化学转移计算中,基于自旋轨道合效应的基于机器学习的校正
Julius B Kleine Büning1, Stefan Grimme1, Markus Bursch2
1Mulliken Center for Theoretical Chemistry, Clausius Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany. grimme@thch.uni-bonn.de.
Physical chemistry chemical physics : PCCP
|January 17, 2024
概括
本研究引入了一种机器学习方法,用于准确预测重原子分子在NMR光谱中的自旋轨道合效应. 这种方法显著提高了13C和1H NMR化学转移的计算精度.
科学领域:
- 计算化学计算化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 核磁共振光谱对于阐明分子结构至关重要.
- 相对论效应,特别是旋转轨道合,对于准确的涉及重原子的NMR计算至关重要.
- 现有的方法很难有效地纳入这些相对论效应.
研究的目的:
- 开发一种机器学习 (ML) 模型,以大致估计旋转轨道 (SO) 合对NMR化学转移的贡献.
- 以最小的计算成本实现准确的相对论NMR计算.
- 调查ML模型对各种重型p块元件的可转移性和性能.
主要方法:
- 使用计算参考数据开发了一种 Δ-机器学习 (Δ-ML) 方法.
- 该模型经过了对6388个结构的自旋轨道零次正规近似 (ZORA) DFT计算进行训练.
- ML模型对13C和1H NMR化学转移的SO贡献进行了近似估计.
主要成果:
- 通过ML方法,可回收13C的~85%的SO贡献,以及1H NMR转换的~70%.
- 这种方法在计算上是廉价的,几乎不会为标量相对论计算增加额外成本.
- 该模型表明可转移到其他DFT方法,并且在有机素和有机化合物上表现良好.
结论:
- 开发的Δ-ML方法显著提高了在重原子存在的情况下NMR化学转移预测的准确性.
- 将这种SO-贡献模型与以前的ML方法相对应效应相结合,可以将从实验值的偏差减半.
- 这项工作为计算化学家在NMR中研究相对论效应提供了一个强大的工具.
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