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VSEPR Theory

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Valence shell electron-pair repulsion theory (VSEPR theory) enables us to predict the molecular structure around a central atom from an examination of the number of bonds and lone electron pairs in its Lewis structure. The VSEPR model assumes that electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between these electron pairs by maximizing the distance between them. The electrons in the valence shell of a central atom form either bonding...

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Updated: Jun 18, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Javier Camarillo-Cisneros¹, Graciela Ramirez-Alonso², Carlos Arzate-Quintana¹

¹Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomedicas, Universidad Autonoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico.

Molecular Diversity

|August 3, 2024

Summary

This summary is machine-generated.

MolGC automates molecular geometry comparison using Density Functional Theory (DFT) data. This tool accurately compares bond lengths, reducing errors in computational chemistry research.

Keywords:

Automated geometry assessment Bond length analysis Computational chemistry DFT Mean absolute error Molecular geometry comparison

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Area of Science:

Computational Chemistry
Materials Science
Quantum Chemistry

Background:

Density Functional Theory (DFT) is crucial for studying molecular and crystal properties.
Comparing DFT-optimized geometries with experimental data is challenging due to manual, error-prone processes.
A need exists for automated tools to compare molecular structures derived from different theoretical levels.

Purpose of the Study:

To introduce MolGC, a Molecular Geometry Comparator algorithm.
To automate the comparison of molecular geometries obtained from various DFT calculations.
To provide accurate bond length comparisons and enhanced visualization tools.

Main Methods:

MolGC calculates the mean absolute error (MAE) of bond lengths.
Integrates data from diverse DFT software packages.
Employs advanced graph labeling for accurate bond identification.
Offers interactive and customizable geometry visualizations.

Main Results:

MolGC achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset.
Demonstrates effectiveness in comparing molecular bond lengths across varying complexities.
Provides automated, statistically summarized MAE computations.

Conclusions:

MolGC offers an automated solution for comparing molecular geometries from DFT.
The algorithm enhances accuracy and efficiency in computational chemistry research.
MolGC facilitates more reliable comparisons between theoretical and experimental molecular structures.