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将机器学习应用于振动光谱学。

Applying Machine Learning to Vibrational Spectroscopy.

作者信息

Fu Weiqiang, Hopkins W Scott

机构信息

Department of Chemistry, University of Waterloo , Waterloo, ON N2L 3G1, Canada.

出版信息

J Phys Chem A. 2018 Jan 11;122(1):167-171. doi: 10.1021/acs.jpca.7b10303. Epub 2017 Dec 19.

Abstract

The low-energy region of the potential energy surface (PES) of the protonated phenylalanine/serine dimer is mapped using the basin-hoping search algorithm, and 37 isomers are identified within 180 kJ·mol of the global-minimum structure. Cluster structures are grouped using hierarchical clustering to partition the PES in terms of nuclear configuration. Calculated IR spectra for the various isomers are then compared with the isomer-specific IR spectra by means of the cosine distance metric to facilitate spectral assignment and identify which regions of the PES are populated in the electrospray ionization process.

摘要

使用盆地跳跃搜索算法绘制了质子化苯丙氨酸/丝氨酸二聚体势能面(PES)的低能区域,并在全局最小结构的180 kJ·mol范围内识别出37种异构体。使用层次聚类对簇结构进行分组,以便根据核构型对PES进行划分。然后,通过余弦距离度量将各种异构体的计算红外光谱与异构体特异性红外光谱进行比较,以促进光谱归属,并确定在电喷雾电离过程中PES的哪些区域存在。

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