Omodemi O, Kaledin M, Kaledin AL. Permutationally invariant polynomial representation of polarizability tensor surfaces for linear regression analysis.
J Comput Chem 2022;
43:1495-1503. [PMID:
35737590 DOI:
10.1002/jcc.26952]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 06/09/2022] [Indexed: 11/07/2022]
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 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.
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