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Permutationally invariant polynomial representation of polarizability tensor surfaces for linear regression analysis.
- Source :
-
Journal of computational chemistry [J Comput Chem] 2022 Aug 15; Vol. 43 (22), pp. 1495-1503. Date of Electronic Publication: 2022 Jun 23. - Publication Year :
- 2022
-
Abstract
- A linearly parameterized functional form for a Cartesian representation of molecular dipole polarizability tensor surfaces (PTS) is described. The proposed expression for the PTS is a linearization of the recently reported power series ansatz of the original Applequist model, which by construction is non-linear in parameter space. This new approach possesses (i) a unique solution to the least-squares fitting problem; (ii) a low level of the computational complexity of the resulting linear regression procedure, comparable to those of the potential energy and dipole moment surfaces; and (iii) a competitive level of accuracy compared to the non-linear PTS model. Calculations of CH <subscript>4</subscript> PTS, with polarizabilities fitted to 9000 training set points with the energies up to 14,000 cm <superscript>-1</superscript> show an impressive level of accuracy of the linear PTS model obtained with ~1600 parameters: ~1% versus 0.3% RMSE for the non-linear vs. linear model on a test set of 1000 configurations.<br /> (© 2022 Wiley Periodicals LLC.)
Details
- Language :
- English
- ISSN :
- 1096-987X
- Volume :
- 43
- Issue :
- 22
- Database :
- MEDLINE
- Journal :
- Journal of computational chemistry
- Publication Type :
- Academic Journal
- Accession number :
- 35737590
- Full Text :
- https://doi.org/10.1002/jcc.26952