On the geometric dependence of the molecular dipole polarizability in water: A benchmark study of higher-order electron correlation, basis set incompleteness error, core electron effects, and zero-point vibrational contributions-Reference-Cited by-同舟云学术

On the geometric dependence of the molecular dipole polarizability in water: A benchmark study of higher-order electron correlation, basis set incompleteness error, core electron effects, and zero-point vibrational contributions

Published:2018-11-28 Issue:20 Volume:149 Page:204303
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Lao Ka Un¹^ORCID,Jia Junteng¹,Maitra Rahul¹,DiStasio Robert A.¹^ORCID

Affiliation:

1. Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA

Funder

College of Arts and Sciences, Cornell University

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5051458

Reference155 articles.

1. Electric-Dipole Polarizabilities of Atoms, Molecules, and Clusters

2. The Theory of Intermolecular Forces

3. Recent Advances in Wave Function-Based Methods of Molecular-Property Calculations

4. Polarizable molecules in the vibrational spectroscopy of water

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Getaway from the Geometry Factor Error in the Molecular Property Calculations: Efficient pecG-n (n = 1, 2) Basis Sets for the Geometry Optimization of Molecules Containing Light p Elements;Journal of Chemical Theory and Computation;2024-07-29

2. Optical $$n(p,\ T_{90})$$ Measurement Suite 2: H$$_2$$O and D$$_2$$O;International Journal of Thermophysics;2024-05-28

3. Comprehensive quantum calculation of the first dielectric virial coefficient of water;The Journal of Chemical Physics;2024-01-12

4. Ab Initio Calculation of Fluid Properties for Precision Metrology;Journal of Physical and Chemical Reference Data;2023-09-01

5. Accurate prediction of global-density-dependent range-separation parameters based on machine learning;The Journal of Chemical Physics;2023-07-24