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Comparison of Molecular Geometry Optimization Methods Based on Molecular Descriptors

Author

Listed:
  • Donatella Bálint

    (Faculty of Chemistry and Chemical Engineering, Babeş-Bolyai University, 11 Arany Janos, 400082 Cluj-Napoca, Romania)

  • Lorentz Jäntschi

    (Faculty of Chemistry and Chemical Engineering, Babeş-Bolyai University, 11 Arany Janos, 400082 Cluj-Napoca, Romania
    Department of Physics and Chemistry, Technical University of Cluj-Napoca, 103-105 Muncii Blvd., 400641 Cluj-Napoca, Romania)

Abstract

Various methods (Hartree–Fock methods, semi-empirical methods, Density Functional Theory, Molecular Mechanics) used to optimize a molecule structure feature the same basic approach but differ in the mathematical approximations used. The geometry optimization procedure calculates the energy at an initial geometry of a molecule and then proceeds to search a new geometry with a lower energy. Using the 3D structures collected from the PubChem database, 20 amino acid geometry optimization calculations were performed with several methods. The purpose of the study was to analyze these methods (39) to find the relationship between them and to determine which to use under different circumstances. Cluster analysis and principal component analysis were performed to evaluate the similarities between the different methods. The results after the analysis can classified into three main groups and can be selected accordingly to solve different types of problems.

Suggested Citation

  • Donatella Bálint & Lorentz Jäntschi, 2021. "Comparison of Molecular Geometry Optimization Methods Based on Molecular Descriptors," Mathematics, MDPI, vol. 9(22), pages 1-12, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2855-:d:676261
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    References listed on IDEAS

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    1. Mitsunori Kayano & Koji Dozono & Sadanori Konishi, 2010. "Functional Cluster Analysis via Orthonormalized Gaussian Basis Expansions and Its Application," Journal of Classification, Springer;The Classification Society, vol. 27(2), pages 211-230, September.
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