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用于NDDO衍生半经验模型的几何校正二次优化算法

Geometry-Corrected Quadratic Optimization Algorithm for NDDO-Descendant Semiempirical Models.

作者信息

Ong Adrian Wee Wen, Cao Steve Yueran, Chan Leemen Chee Yong, Lim Javier, Kwek Leong Chuan

机构信息

Centre for Quantum Technologies, National University of Singapore, Singapore 117543, Singapore.

MajuLab, CNRS-UNS-NUS-NTU International Joint Research Unit, UMI 3654, Singapore 117543, Singapore.

出版信息

J Chem Theory Comput. 2025 Jan 14;21(1):138-154. doi: 10.1021/acs.jctc.4c01070. Epub 2024 Dec 18.

DOI:10.1021/acs.jctc.4c01070
PMID:39694476
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11736688/
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.

摘要

长期以来一直认为，在不进行精确几何优化的情况下就可以对NDDO衍生的半经验方法的参数进行优化，本文对此进行了详细评估；随后给出了用于分析评估分子性质的几何校正导数的相关方程，这些导数考虑了最佳几何结构的变化。从我们实现的MNDO计算得到的一阶和二阶导数，被用于对从PM7训练集中选取的1113个CHNO分子进行有限的重新参数化，结果表明相较于用于PMx方法参数优化的PARAM程序有了改进。

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