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Published in:
Fragment Molecular Orbital Method as Cluster Expansion Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Group Molecular Orbital Method and Python-Based Programming Approach

Author : Tomomi Shimazaki

Published in: Recent Advances of the Fragment Molecular Orbital Method

Publisher: Springer Singapore

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Abstract

In this chapter, an algorithm to solve the Huzinaga subsystem self-consistent field equations is described, together with two approximations: a local expansion of subsystem molecular orbitals and a truncation of the projection operator. We have referred to the algorithm as the group molecular orbital (GMO) method, and its theoretical concept is based on fragmenting and dividing molecular orbitals, similar to the fragment molecular orbital (FMO) method. However, the GMO method can define a Hamiltonian for each molecular group (fragment), unlike FMO. In addition, we discuss a Python-based programming approach to efficiently implement the GMO algorithm. Python can provide several advantages in program development, including flexibility and high productivity, easy integration for different algorithms, and abundant tools and libraries. We also present that the Python-based approach does not sacrifice the computational performance of quantum chemistry calculations.

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previous chapter Application of the Fragment Molecular Orbital Method to Organic Charge Transport Materials in Xerography: A Feasibility Study and a Charge Mobility Analysis
next chapter Multi-Level Parallelization of the Fragment Molecular Orbital Method in GAMESS
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Metadata
Title
Group Molecular Orbital Method and Python-Based Programming Approach
Author
Tomomi Shimazaki
Copyright Year
2021
Publisher
Springer Singapore
Book
Recent Advances of the Fragment Molecular Orbital Method

Print ISBN: 978-981-15-9234-8

Electronic ISBN: 978-981-15-9235-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-9235-5_29

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