Predict the Polarizability and Order of Magnitude of Second Hyperpolarizability of Molecules by Machine Learning

被引:5
|
作者
Zhao, Guoxiang [1 ,2 ]
Yan, Weiyin [1 ,2 ]
Wang, Zirui [1 ,3 ]
Kang, Yao [1 ]
Ma, Zuju [4 ]
Gu, Zhi-Gang [1 ,5 ]
Li, Qiao-Hong [1 ]
Zhang, Jian [1 ]
机构
[1] Chinese Acad Sci, Fujian Inst Res Struct Matter, State Key Lab Struct Chem, Fuzhou 350002, Fujian, Peoples R China
[2] Fuzhou Univ, Sch Chem, Fuzhou 350108, Fujian, Peoples R China
[3] ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai 201210, Peoples R China
[4] Yantai Univ, Sch Environm & Mat Engn, Yantai 264005, Peoples R China
[5] Fujian Sci & Technol Innovat Lab Optoelectroninc I, Fuzhou 350108, Fujian, Peoples R China
来源
JOURNAL OF PHYSICAL CHEMISTRY A | 2023年 / 127卷 / 29期
关键词
NONLINEAR-OPTICAL PROPERTIES;
D O I
10.1021/acs.jpca.2c08563
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In order to determine the polarizability and hyperpolarizabilityof a molecule, several key parameters need to be known, includingthe excitation energy of the ground and excited states, the transitiondipole moment, and the difference of dipole moment between the groundand excited states. In this study, a machine-learning model was developedand trained to predict the molecular polarizability and second-orderhyperpolarizability on a subset of QM9 data set. The density of stateswas employed as input to the model. The results demonstrated thatthe machine-learning model effectively estimated both polarizabilityand the order of magnitude of second-order hyperpolarizability. However,the model was unable to predict the dipole moment and first-orderhyperpolarizability, suggesting limitations in its ability to predictthe difference of dipole moment between the ground and excited states.The computational efficiency of machine-learning models compared totraditional quantum mechanical calculations enables the possibilityof large-scale screening of molecules that satisfy specific requirementsusing existing databases. This work presents a potential solutionfor the efficient exploration and analysis of molecules on a largerscale.
引用
收藏
页码:6109 / 6115
页数:7
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