机器学习预测无声频率:对模型和可转移性的研究
Jamoliddin Khanifaev1, Tim Schrader1, Eva Perlt1
1Friedrich Schiller University Jena, Löbdergraben 32, 07743 Jena, Germany. eva.von.domaros@uni-jena.de.
Physical chemistry chemical physics : PCCP
|September 2, 2024
概括
这项研究使用机器学习预测了分子的非和频率. 梯度增强回归准确地估计了这些特性,对较大的分子显示出希望.
科学领域:
- 计算化学计算化学
- 物理化学 物理化学
- 化学物理 化学物理
背景情况:
- 精确的热化学性质需要不和的效应.
- 在物理和化学物理学中,描述非和性至关重要.
- 电子结构方法越来越准确,需要先进的后处理.
研究的目的:
- 为了计算化和化碳的无和频率.
- 使用机器学习开发对无声频率的预测模型.
- 评估这些模型对新化学系统的可转移性.
主要方法:
- 使用正常模式分析和振动自相一致场 (VSCF) 计算非和频率.
- 通过多线性回归和梯度增强回归来开发预测模型.
- 使用基于和模型的描述符进行回归分析.
主要成果:
- 梯度增强回归准确地预测了不协调的频率.
- 多线性回归提供了合理的预测,捕获模式对模式的合.
- 机器学习模型表明可转移到更大,看不见的分子.
结论:
- 机器学习模型,特别是梯度增强,可以可靠地预测无声频率.
- 简单的回归模型为不协调行为提供了有价值的见解.
- 开发的模型显示出适用于各种化学系统的优秀潜力.
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