Accurate Energies and Structures for Large Water Clusters Using the X3LYP Hybrid Density Functional
- Creators
- Su, Julius T.
- Xu, Xin
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Goddard, William A., III
Abstract
We predict structures and energies of water clusters containing up to 19 waters with X3LYP, an extended hybrid density functional designed to describe noncovalently bound systems as accurately as covalent systems. Our work establishes X3LYP as the most practical ab initio method today for calculating accurate water cluster structures and energies. We compare X3LYP/aug-cc-pVTZ energies to the most accurate theoretical values available (n = 2−6, 8), MP2 with basis set superposition error (BSSE) corrections extrapolated to the complete basis set limit. Our energies match these reference energies remarkably well, with a root-mean-square difference of 0.1 kcal/mol/water. X3LYP also has ten times less BSSE than MP2 with similar basis sets, allowing one to neglect BSSE at moderate basis sizes. The net result is that X3LYP is ∼100 times faster than canonical MP2 for moderately sized water clusters.
Additional Information
© 2004 American Chemical Society. Received 9 June 2004. Published online 2 November 2004. Published in print 1 November 2004. This research was funded partially by NSF (CHE 9985574), by NIH (HD 36385-02), and by DOE-ASCI. The facilities of the Materials and Process Simulation Center used in these studies were funded by ARO-DURIP, ONR-DURIP, NSF-MRI, a SUR grant from IBM, and the Beckman Institute. In addition, the Materials and Process Simulation Center is funded by grants from ARO-MURI, ONR-MURI, ONR-DARPA, NIH, NSF, General Motors, ChevronTexaco, Seiko-Epson, and Asahi Kasei. We thank Mr. Christopher L. McClendon for initial suggestions and Prof. Jian Wan, Central China Normal University, for helping with some of the calculations.Attached Files
Supplemental Material - jp047502_2Bsi20040810_082450.pdf
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Additional details
- Eprint ID
- 77728
- DOI
- 10.1021/jp047502+
- Resolver ID
- CaltechAUTHORS:20170524-144024380
- NSF
- CHE 9985574
- NIH
- HD 36385-02
- Department of Energy (DOE)
- Army Research Office (ARO)
- Office of Naval Research (ONR)
- IBM
- Caltech Beckman Institute
- Defense Advanced Research Projects Agency (DARPA)
- General Motors
- ChevronTexaco
- Seiko-Epson
- Asahi Kasei
- Created
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2017-05-24Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field