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Role of Atomic and Molecular Non-observable Properties in the Understanding and Description of Real Observables of the Chemical Systems. A Review

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Computational Bioengineering and Bioinformatics (ICCB 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 11))

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Abstract

Chemical species can be characterized by various observable features: mass, enthalpy of formation, charge (ions), dipole moment, magnetic susceptibility, electrical susceptibility, electromagnetic spectra, refraction index, polarizability, electron density distribution etc. But, on the other hand, the understanding of chemical an physical behavior is usually based on specific non-observable features - for example: electronegativity, partial atomic charges, nucleophilicity, atomic and molecular orbitals, aromaticity, hyperconjugation, … All non-observable features generally have no physical unit, and are not amenable to experimental measurements. For that reason the values ascribed to them are strongly dependent on the definition(s). For example, we know (and use) various electronegativity scales: Pauling’s, Mulliken’s, Alfred-Rochov’s [1, 2], Sanderson’s, Allen’s, and other. They are based on different theoretical assumptions, and produce (on many instances, significantly) different numerical values. On the other hand, all scales follow similar general trend, indicating that the values reflect some intrinsic chemical property.

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Acknowledgments

Ministry of Education, Science, and Technological Development of the Republic of Serbia supported this work, Grant No. 172035. Author gratefully acknowledge the computational time provided on the PARADOX cluster at the Scientific Computing Laboratory of the Institute of Physics, Belgrade.

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  1. University of Belgrade, IChTM, Centre for Chemistry, Studentski trg 16, 11000, Belgrade, Serbia

    Ivan Juranić

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  1. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia

    Nenad Filipovic

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Juranić, I. (2020). Role of Atomic and Molecular Non-observable Properties in the Understanding and Description of Real Observables of the Chemical Systems. A Review. In: Filipovic, N. (eds) Computational Bioengineering and Bioinformatics. ICCB 2019. Learning and Analytics in Intelligent Systems, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-43658-2_1

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  • DOI: https://doi.org/10.1007/978-3-030-43658-2_1

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