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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

Computational chemistry methods based on MNDO and tools for improving their accuracy.

James J P Stewart¹

¹Stewart Computational Chemistry, 15210 Paddington Circle, Colorado Springs, CO, 80921, USA. mrmopac@att.net.

Journal of Molecular Modeling

|March 13, 2026

Summary

The Modified Neglect of Diatomic Overlap (MNDO) approximation has a long history in computational chemistry, with ongoing evolution and improvements. This review details its development and provides resources for future advancements in semiempirical methods.

Keywords:

Applicability Causes of error Development of PM6-ORG MNDO Modeling proteins Semiempirical method development

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Area of Science:

Computational Chemistry
Theoretical Chemistry

Background:

Semiempirical computational methods have a rich history, originating with the Modified Neglect of Diatomic Overlap (MNDO) approximation released in 1977.
The MNDO method has undergone continuous development, leading to improved forms such as AM1 and PM3.
Further evolution of MNDO-based methods is expected in the future.

Purpose of the Study:

To provide a historical overview of MNDO-based computational methods.
To describe the mechanisms driving the development of current MNDO approximations.
To offer resources for researchers to facilitate the creation of novel MNDO methods.

Main Methods:

Historical review of MNDO approximation development.
Description of the underlying mechanisms for creating current MNDO methods.
Provision of supplementary files detailing development steps and procedures.

Main Results:

The evolution of MNDO methods from 1977 to the present has yielded significant improvements.
Understanding the development mechanisms is key to advancing semiempirical computational techniques.
Supplementary materials are available to aid in the creation of future MNDO methods.

Conclusions:

MNDO-based methods represent a continuously evolving area of computational chemistry.
The historical progression highlights the importance of iterative refinement in developing accurate semiempirical models.
The provided resources aim to empower future research and development in this field.