Geometry-corrected quadratic optimization algorithm for NDDO-Descendant Semiempirical Models

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Type
Article
Citation
Ong, A. W. W., Cao, S. Y., Chan, L. C. Y., Lim, J., & Kwek, L. C. (2024). Geometry-corrected quadratic optimization algorithm for NDDO-Descendant Semiempirical Models. Journal of Chemical Theory and Computation, 21(1), 138–154. https://doi.org/10.1021/acs.jctc.4c01070
Author
Ong, Adrian Wee Wen
•
Cao, Steve Yeuran
•
Chan, Leemen Chee Yong
•
Lim, Javier
•
Kwek, Leong Chuan 
Abstract
The long-held assumption that the optimization of parameters for NDDO-descendant semiempirical methods may be performed without precise geometry optimization is assessed in detail; the relevant equations for the analytical evaluation of the geometry-corrected derivatives of molecular properties that account for changes in the optimum geometry are then presented. The first and second derivatives calculated from our implementation of MNDO are used for a limited reparameterization of 1,113 CHNO molecules taken from the PM7 training set, demonstrating an improvement over the PARAM program used in the optimization of parameters for the PMx methods.
Keywords
Date Issued
2024
Publisher
American Chemical Society
Journal
Journal of Chemical Theory and Computation
DOI
10.1021/acs.jctc.4c01070
Description
The open access publication is available at: https://doi.org/10.1021/acs.jctc.4c01070