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CS2 的从头势能和偶极矩表面:分子振动能的确定。

Ab initio potential energy and dipole moment surfaces for CS2: determination of molecular vibrational energies.

机构信息

Department of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2 Canada.

出版信息

J Phys Chem A. 2013 Aug 15;117(32):6925-31. doi: 10.1021/jp309651r. Epub 2012 Dec 14.

Abstract

The ground state potential energy and dipole moment surfaces for CS2 have been determined at the CASPT2/C:cc-pVTZ,S:aug-cc-pV(T+d)Z level of theory. The potential energy surface has been fit to a sum-of-products form using the neural network method with exponential neurons. A generic interface between neural network potential energy surface fitting and the Heidelberg MCTDH software package is demonstrated. The potential energy surface has also been fit using the potfit procedure in MCTDH. For fits to the low-energy regions of the potential, the neural network method requires fewer parameters than potfit to achieve high accuracy; global fits are comparable between the two methods. Using these potential energy surfaces, the vibrational energies have been computed for the four most abundant CS2 isotopomers. These results are compared to experimental and previous theoretical data. The current potential energy surfaces are shown to accurately reproduce the low-lying vibrational energies within a few wavenumbers. Hence, the potential energy and dipole moments surfaces will be useful for future study on the control of quantum dynamics in CS2.

摘要

CS2 的基态势能和偶极矩面已在 CASPT2/C:cc-pVTZ、S:aug-cc-pV(T+d)Z 理论水平上确定。使用具有指数神经元的神经网络方法,将势能面拟合为乘积和的形式。展示了神经网络势能面拟合与海德堡 MCTDH 软件包之间的通用接口。还使用 MCTDH 中的 potfit 程序对势能面进行了拟合。对于势能的低能区域的拟合,神经网络方法比 potfit 需要更少的参数即可达到高精度;两种方法的全局拟合相当。使用这些势能面,计算了四个最丰富的 CS2 同位素的振动能。将这些结果与实验和以前的理论数据进行了比较。当前的势能面被证明可以在几个波数内准确地再现低能振动能。因此,这些势能面和偶极矩面将有助于未来研究 CS2 中量子动力学的控制。

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