An Improved Parameterization Procedure for NDDO-Descendant Semiempirical Methods

TL;DR

This paper introduces an improved parameterization procedure for NDDO-descendant semiempirical methods, utilizing an exact parameter Hessian for better reparameterization of MNDO models based on extensive molecular data.

Contribution

It presents an analytic approach to evaluate derivatives and employs the exact Hessian for reparameterizing MNDO models, enhancing their accuracy and reliability.

Findings

Exact parameter Hessian improves reparameterization accuracy.

Reparameterization using 1206 molecules enhances model performance.

Method facilitates better modeling of large, complex systems.

Abstract

MNDO-based semiempirical methods in quantum chemistry have found widespread application in the modelling of large and complex systems. A method for the analytic evaluation of first and second derivatives of molecular properties against semiempirical parameters in MNDO-based NDDO-descendant models is presented, and the resultant parameter Hessian is compared against the approximant currently used in parameterization for the PMx models. As a proof of concept, the exact parameter Hessian is employed in a limited reparameterization of MNDO for the elements C, H, N, O and F using 1206 molecules for reference data.