Suppr超能文献

通过新实验数据库利用密度泛函理论改进红外光谱预测

Improved Infrared Spectra Prediction by DFT from a New Experimental Database.

作者信息

Katari Madanakrishna, Nicol Edith, Steinmetz Vincent, van der Rest Guillaume, Carmichael Duncan, Frison Gilles

机构信息

LCM, CNRS, Ecole Polytechnique, Université Paris-Saclay, 91128, Palaiseau, France.

Laboratoire de Chimie Physique, Université Paris Sud, CNRS, 91405, Orsay, France.

出版信息

Chemistry. 2017 Jun 22;23(35):8414-8423. doi: 10.1002/chem.201700340. Epub 2017 Apr 26.

Abstract

This work aims to improve the computation of infrared spectra of gas-phase cations using DFT methods. Experimental infrared multiple photon dissociation (IRMPD) spectra for ten Zn and Ru organometallic complexes have been used to provide reference data for 64 vibrational modes in the 900-2000 cm range. The accuracy of the IR vibrational frequencies predicted for these bands has been assessed over five DFT functionals and three basis sets. The functionals include the popular B3LYP and M06-2X hybrids and the range-separated hybrids (RSH) CAM-B3LYP, LC-BLYP, and ωB97X-D. B3LYP gives the best mean absolute error (MAE) and root-mean-square error (RMSE) values of 7.1 and 9.6 cm , whilst the best RSH functional, ωB97X-D, gives 12.8 and 16.6 cm , respectively. Using linear correlations instead of scaling factors improves the prediction accuracy significantly for all functionals. Experimental and computed spectra for a single complex can show significant differences even when the molecular structure is calculated correctly, and a means of defining confidence limits for any given computed structure is also provided.

摘要

这项工作旨在利用密度泛函理论(DFT)方法改进气相阳离子红外光谱的计算。已使用十种锌和钌有机金属配合物的实验红外多光子解离(IRMPD)光谱为900 - 2000 cm范围内的64种振动模式提供参考数据。针对这些谱带预测的红外振动频率的准确性已在五种DFT泛函和三种基组上进行了评估。这些泛函包括常用的B3LYP和M06 - 2X杂化泛函以及范围分离杂化泛函(RSH)CAM - B3LYP、LC - BLYP和ωB97X - D。B3LYP给出的最佳平均绝对误差(MAE)和均方根误差(RMSE)值分别为7.1和9.6 cm,而最佳的RSH泛函ωB97X - D给出的值分别为12.8和16.6 cm。对于所有泛函,使用线性相关性而非比例因子可显著提高预测准确性。即使分子结构计算正确,单个配合物的实验光谱和计算光谱仍可能显示出显著差异,并且还提供了一种为任何给定计算结构定义置信限的方法。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验