通过气相DFT预测PNMR在大型异质数据库中的转移:调整器和溶剂效应的影响:气相DFT:调整器和溶剂效应的影响
Robert Geitner1, Christian Dreßler2
1Group for Physical Chemistry/Catalysis, Department of Mathematics and Natural Sciences, Institute of Chemistry and Bioengineering, Technische Universität Ilmenau, Weimarer Str. 32, 98693 Ilmenau, Germany.
ACS omega
|February 9, 2026
概括
这项研究通过量子化学计算丰富了实验性31P核磁共振 (NMR) 数据,为改进机器学习模型和化合物的结构阐明创造了大量数据集.
科学领域:
- 计算化学的计算化学
- 频谱学是一种光谱学.
- 数据科学数据科学数据科学
背景情况:
- Ilm-NMR-P31数据库包含实验性P NMR数据.
- 将这个数据集扩展到理论信息可以提高它的实用性.
- 机器学习 (ML) 模型需要全面和准确的数据进行开发.
研究的目的:
- 为了丰富Ilm-NMR-P31数据集与量子化学衍生的P化学转移和分子几何学.
- 评估用于预测P NMR转移的计算方法的准确性.
- 促进用于结构阐明的先进ML模型的开发.
主要方法:
- 在BLYP和B3LYP水平使用密度函数理论 (DFT) 计算.
- 优化了分子几何形状,并计算了10007种化合物的P化学转移.
- 研究了符合性采样和隐性溶剂模型以提高准确性.
主要成果:
- 创建了一个混合数据集,将实验和量子化学数据结合起来.
- 计算轮班的根平均平方误差 (RMSE) 为30.82 ppm (真空),并通过符合性采样改进为29.37 ppm.
- B3LYP功能证明了足够的准确性和广泛适用于常规NMR转移预测.
结论:
- 量子化学丰富NMR数据显著增强实验数据集.
- 符合性采样对于提高预测NMR转移的准确性至关重要.
- B3LYP功能是大规模P NMR转移预测的可靠选择,支持ML应用.
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