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This study challenges the assumption that precise geometry optimization is unnecessary for semiempirical methods. It introduces geometry-corrected derivatives, improving parameter optimization for NDDO-descendant methods.

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Area of Science:

  • * Computational Chemistry
  • * Quantum Chemistry
  • * Theoretical Chemistry

Background:

  • * Semiempirical quantum chemistry methods, particularly those descending from the Neglect of Diatomic Differential Overlap (NDDO) approximation, are widely used for molecular modeling.
  • * A common practice has been to optimize method parameters without rigorous geometric optimization, potentially impacting accuracy.
  • * The accuracy of these methods relies heavily on the quality of their parameterization.

Purpose of the Study:

  • * To rigorously assess the assumption that parameter optimization for NDDO-descendant semiempirical methods can be done without precise geometry optimization.
  • * To develop and present equations for analytically evaluating geometry-corrected derivatives of molecular properties.
  • * To demonstrate the utility of these derivatives in improving parameterization strategies.

Main Methods:

  • * Detailed theoretical analysis of geometry-corrected derivatives for molecular properties.
  • * Implementation of Modified Neglect of Diatomic Differential Overlap (MNDO) for calculating first and second derivatives.
  • * Application of these derivatives to reparameterize a subset of 1,113 CHNO molecules from the PM7 training set.

Main Results:

  • * The study provides a detailed assessment of the impact of geometry optimization on parameterization.
  • * Analytical equations for geometry-corrected derivatives were derived and implemented.
  • * Reparameterization using the new method showed improvements compared to the standard PARAM program for PMx methods.

Conclusions:

  • * Precise geometry optimization is crucial for accurate parameterization of NDDO-descendant semiempirical methods.
  • * The developed geometry-corrected derivative approach offers a more robust method for parameter optimization.
  • * This work contributes to the development of more accurate and reliable semiempirical models for computational chemistry.