PIPFit 2022
Loading...
Persistent link to this item
Statistics
View StatisticsCollection period
Date completed
2021-01-17
Date updated
Time period coverage
Geographic coverage
Source information
Journal Title
Journal ISSN
Volume Title
Title
PIPFit 2022
Published Date
2022-02-14
Author Contact
Truhlar, Donald G
truhlar@umn.edu
truhlar@umn.edu
Type
Dataset
Software
Software, including Manual
Software
Software, including Manual
Abstract
The PIPFit 2022 program can be used to develop analytic representations of potential energy surfaces for three-body and four-body systems. A weighted least-squares fit is performed with permutationally invariant polynomials (PIPs) whose variables are Morse-like bond functions, Gaussians, mixed exponential–Gaussians (MEGs), or hyperbolic secant variables. Three kinds of fit can be performed with the program:
*PIPs fit to the whole potential, as originally proposed by Braams, Bowman, and Xie,
*connected PIPs fit to the whole potential after removing the unconnected terms,
*connected PIPs fit to the many-body part of the potential after removing the unconnected terms and the two-body terms.
The program can also perform a two-stage fit in which one first fits lower-level energetic data with a large number of geometries and then fits higher-level corrections with a smaller set of geometries.
*PIPs fit to the whole potential, as originally proposed by Braams, Bowman, and Xie,
*connected PIPs fit to the whole potential after removing the unconnected terms,
*connected PIPs fit to the many-body part of the potential after removing the unconnected terms and the two-body terms.
The program can also perform a two-stage fit in which one first fits lower-level energetic data with a large number of geometries and then fits higher-level corrections with a smaller set of geometries.
Description
Referenced by
Y. Paukku, K. R. Yang, Z. Varga, and D. G. Truhlar J. Chem. Phys. 139, 044309 (2013).
J. D. Bender, P. Valentini, I. Nompelis, Y. Paukku, Z. Varga, D. G. Truhlar, T. Schwartzentruber, G. V. Candler, J. Chem. Phys. 143, 054304 (2015).
Y. Shu, J. Kryven, A. G. S. de Oliveira-Filho, L. Zhao, G.-L. Song, S. L. Li, R. Meana-Pañeda, B. Fu, J. M. Bowman, and D. G. Truhlar, J. Chem. Phys. 151, 104311 (2019).
J. D. Bender, P. Valentini, I. Nompelis, Y. Paukku, Z. Varga, D. G. Truhlar, T. Schwartzentruber, G. V. Candler, J. Chem. Phys. 143, 054304 (2015).
Y. Shu, J. Kryven, A. G. S. de Oliveira-Filho, L. Zhao, G.-L. Song, S. L. Li, R. Meana-Pañeda, B. Fu, J. M. Bowman, and D. G. Truhlar, J. Chem. Phys. 151, 104311 (2019).
Related to
Replaces
item.page.isreplacedby
Publisher
Collections
Funding information
U. S. Air Force Office of Scientific Research under Grant Nos. FA9550-10-1-0563, FA9550-16-1-0161, and FA9550-19-1-0219
U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award No. DE-SC0015997
U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award No. DE-SC0015997
item.page.sponsorshipfunderid
item.page.sponsorshipfundingagency
item.page.sponsorshipgrant
Previously Published Citation
Other identifiers
Suggested citation
Yang, Ke R; Varga, Zoltan; Parker, Kelsey A; Shu, Yinan; Truhlar, Donald G. (2022). PIPFit 2022. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/tnzc-nt34.
View/Download File
File View/Open
Description
Size
PIPFit2022.tar.gz
Source code, tests, and manual
(7.99 MB)
220213_PIPFit_2022_Manual.pdf
Manual, CC-BY-4.0
(733.66 KB)
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.