Permutationally invariant polynomial representation of polarizability tensor surfaces for linear regression analysis

© 2022 Wiley Periodicals LLC.

Bibliographische Detailangaben
Veröffentlicht in:Journal of computational chemistry. - 1984. - 43(2022), 22 vom: 15. Aug., Seite 1495-1503
1. Verfasser: Omodemi, Oluwaseun (VerfasserIn)
Weitere Verfasser: Kaledin, Martina, Kaledin, Alexey L
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2022
Zugriff auf das übergeordnete Werk:Journal of computational chemistry
Schlagworte:Journal Article Applequist model ab initio molecular dynamics linear regression permutationally invariant polynomials polarizability tensor
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520 |a 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 CH4 PTS, with polarizabilities fitted to 9000 training set points with the energies up to 14,000 cm-1 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 
650 4 |a Journal Article 
650 4 |a Applequist model 
650 4 |a ab initio molecular dynamics 
650 4 |a linear regression 
650 4 |a permutationally invariant polynomials 
650 4 |a polarizability tensor 
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700 1 |a Kaledin, Alexey L  |e verfasserin  |4 aut 
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