氨基酸和的拉曼光谱来自机器学习的极化性
Ethan Berger1, Juha Niemelä2, Outi Lampela3
1Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, P.O. Box 4500, Oulu FIN-90014, Finland.
机器学习模型准确地预测了拉曼光谱模拟的氨基酸极化性. 这些模型显示了对的改进可转移性,增强了计算化学中的振动分析.
科学领域:
- 计算化学是一种计算化学.
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 拉曼光谱分析分子振动和组成.
- 模拟拉曼光谱依赖于电子极化性,通常来自第一原则数据.
- 目前的机器学习 (ML) 模型面临的挑战是将知识从小分子转移到比如类更大的结构,因为计算成本很高.
研究的目的:
- 开发和评估ML模型来预测氨基酸极化性.
- 评估这些ML模型对更大的质结构的可转移性.
- 模拟和分析氨基酸和小的拉曼光谱.
主要方法:
- 在第一原则数据上训练两个ML模型 (包括一个神经网络),以预测所有20个氨基酸的极化性.
- 将ML模型与密度函数理论 (DFT) 计算进行比较.
- 将ML预测的极化度与经典力场分子动态相结合,用于拉曼光谱模拟.
主要成果:
- 与另一种ML方法相比,神经网络模型在预测氨基酸极化性方面表现出卓越的可转移性.
- 对氨基酸的模拟拉曼光谱与实验数据有很好的一致性.
- 将键结构纳入训练集显著提高了的预测准确性,即使是那些不在训练数据中的.
结论:
- 机器学习模型,特别是神经网络,可以有效地预测氨基酸拉曼光谱的极化性.
- 开发的模型显示有希望的可转移到结构,克服直接DFT计算的局限性.
- 这种方法可以为生物分子提供准确的计算拉曼光谱分析.
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