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Andrey A. Dobrynin

Bio: Andrey A. Dobrynin is an academic researcher from Russian Academy of Sciences. The author has contributed to research in topics: Wiener index & Topological index. The author has an hindex of 14, co-authored 60 publications receiving 2120 citations. Previous affiliations of Andrey A. Dobrynin include Novosibirsk State University & University of Kragujevac.


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TL;DR: The Wiener index W is the sum of distances between all pairs of vertices of a (connected) graph as discussed by the authors, defined as the distance between all vertices in a graph.
Abstract: The Wiener index W is the sum of distances between all pairs of vertices of a (connected) graph. The paper outlines the results known for W of trees: methods for computation of W and combinatorial expressions for W for various classes of trees, the isomorphism–discriminating power of W, connections between W and the center and centroid of a tree, as well as between W and the Laplacian eigenvalues, results on the Wiener indices of the line graphs of trees, on trees extremal w.r.t. W, and on integers which cannot be Wiener indices of trees. A few conjectures and open problems are mentioned, as well as the applications of W in chemistry, communication theory and elsewhere.

1,015 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present the results known for W of the HS: method for computing W, expressions relating W with the structure of the respective HS, results on HS's extremal w.r.t. W, and on integers that cannot be the W-values of HS's.
Abstract: The Wiener index W is the sum of distances between all pairs of vertices of a (connected) graph. Hexagonal systems (HS's) are a special type of plane graphs in which all faces are bounded by hexagons. These provide a graph representation of benzenoid hydrocarbons and thus find applications in chemistry. The paper outlines the results known for W of the HS: method for computation of W, expressions relating W with the structure of the respective HS, results on HS's extremal w.r.t. W, and on integers that cannot be the W-values of HS's. A few open problems are mentioned. The chemical applications of the results presented are explained in detail.

371 citations

Journal ArticleDOI
TL;DR: Trees with minimal and maximal hyper-Wiener indices (WW) are determined among n-vertex trees, minimum and maximum WW is achieved for the star-graph and the path-graph, respectively.
Abstract: Trees with minimal and maximal hyper-Wiener indices (WW) are determined: Among n-vertex trees, minimum and maximum WW is achieved for the star-graph (Sn) and the path-graph (Pn), respectively. Since WW(Sn) is a quadratic polynomial in n,, whereas WW(Pn) is a quartic polynomial in n, the hyper-Wiener indices of all n-vertex trees assume values from a relatively narrow interval. Consequently, the hyper-Wiener index must have a very low isomer-discriminating power. This conclusion is corroborated by finding large families of trees, all members of which have equal WW-values.

49 citations


Cited by
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Journal ArticleDOI
TL;DR: The Wiener index W is the sum of distances between all pairs of vertices of a (connected) graph as discussed by the authors, defined as the distance between all vertices in a graph.
Abstract: The Wiener index W is the sum of distances between all pairs of vertices of a (connected) graph. The paper outlines the results known for W of trees: methods for computation of W and combinatorial expressions for W for various classes of trees, the isomorphism–discriminating power of W, connections between W and the center and centroid of a tree, as well as between W and the Laplacian eigenvalues, results on the Wiener indices of the line graphs of trees, on trees extremal w.r.t. W, and on integers which cannot be Wiener indices of trees. A few conjectures and open problems are mentioned, as well as the applications of W in chemistry, communication theory and elsewhere.

1,015 citations

01 Jan 2016
TL;DR: “Essentials” covers force field and molecular orbital theory, Monte Carlo and Molecular Dynamics simulations, thermodynamic and electronic (spectroscopic) property calculation, condensed phase treatment and a few more topics, and is an alternative to Andrew R. Leach's well-established “Molecular Modeling” and Frank Jensen’s “Introduction to Computational Chemistry”.
Abstract: The fact that a new text book introducing the essentials of computational chemistry contains more than 500 pages shows impressively the grown and still growing size and importance of this field of chemistry. The author’s objectives of the book, using his own words, are “to provide a survey of computational chemistry its underpinnings, its jargon, its strengths and weaknesses that will be accessible to both the experimental and theoretical communities”. This design as a general introduction into computational chemistry makes it an alternative to Andrew R. Leach’s well-established “Molecular Modeling” (Prentice Hall) and Frank Jensen’s “Introduction to Computational Chemistry” (Wiley), although the latter focuses on the theory of electronic structure methods. Cramer’s “Essentials” covers force field and molecular orbital theory, Monte Carlo and Molecular Dynamics simulations, thermodynamic and electronic (spectroscopic) property calculation, condensed phase treatment and a few more topics. Moreover, the book contains thirteen selected case studies sexamples taken from the literature sto illustrate the application of the just presented theoretical and computational models. This especially makes the text book well suited for both classroom discussion and self-study. Each chapter of “Essentials” covers a main topic of computational chemistry and will be briefly described here; all chapters are ended by a bibliography and suggested additional readings as well as the literature references cited in the text. In chapter 1 the author defines basic terms such as “theory”, “model”, and “computation”, introduces the concept of the potential energy surface and provides some general considerations about hardware and software. Interestingly, the first equation occurring in the text is not Schro ̈dinger’s equation, as is the case for most computational chemistry introductions, but the famous Einstein relation. The second chapter deals with molecular mechanics. It explains the different potential energy contributions, introduces the field of structure optimization, and provides an overview of the variety of modern force fields. Chapter 3 covers the simulation of molecular ensembles. It defines phase space and trajectories and shows the formalism of, and problems and difference between, Monte Carlo and molecular dynamics. In chapter 4 the author introduces the foundations of molecular orbital theory. Basic concepts such as Hamilton operator, LCAO basis set approach, many-electron wave functions, etc. are explained. To illuminate the LCAO variational process, the Hu ̈ckel theory is presented with an example. Chapter 5 deals with semiempirical molecular orbital (MO) theory. Besides the classical approaches (extended Hu ̈ckel, CNDO, INDO, NDDO) and methods (e.g., MNDO, AM1, PM3) and their performance, examples are provided from the ongoing development in that still fascinating area. Ab initio MO theory is presented in chapter 6; the basis set concept is discussed in detail, and, after some considerations from an user’s point of view, the general performance of ab initio methods is explicated. The next chapter covers the problem of electron correlation and gives the most prominent solutions for its treatment: configuration interaction, theory of the multiconfiguration self-consistent field, perturbation, and coupled cluster. Practical issues are also discussed. Chapter 8’s topic is density functional theory (DFT). Its theoretical foundation, methodology, and some functionals as well as its pros and cons compared to MO theory are presented together with a general performance overview. The next two chapters deal with charge distribution, derived and spectroscopic properties (e.g., atomic charges, polarizability, rotational, vibrational, and NMR spectra), and thermodynamic properties (e.g., zero-point vibrational energy, free energy of formation, and reaction). The modeling of condensed phases is addressed in chapters 11 (implicit models) and 12 (explicit models), which closes with a comparison between the two approaches. Chapter 13 familiarizes the reader with hybrid quantum mechanical/molecular mechanical (QM/MM) models. Polarization as well as the problematic implications of unsaturated QM and MM components are discussed, and empirical valence bond methods are also presented. The treatment of excited states is the topic of chapter 14; besides CI and MCSCF as computational methods, transition probabilities and solvatochromism are discussed. The last chapter deals with reaction dynamics, mostly adiabaticskinetics, rate constants, reaction paths, and transition state theory are section topics here sbut also nonadiabatic, introducing curve crossing and Marcus theory in brief. The appendix is divided into four parts: an acronym glossary (which is very helpful), an overview of symmetry and group theory, an introduction to spin algebra, and finally a section about orbital localization. A rather detailed index ends the book. The “Essentials” writing style fits the fascinating topic: one reads on and on andssurprise! sanother chapter has been absorbed. The text is complemented by a large number of black and white figures and clear tables, mostly self-explanatory with descriptive captions. The use of equations and mathematical formulas in general is well-balanced, and the level of math should be understandable for every natural scientist with some basic knowledge of physics. There are only a few minor shortcomings: for example, a literature reference cited in the text (“Beck et al.”, p 142) is missing in the bibliography; “Kronecker” is mistyped with o ̈; and the author completely forgot to reference Leach’s text book when referring to other computational chemistry introductions. However, the author has established a specific errata web page (http://pollux.chem.umn.edu/ ∼cramer/Errors.html) with all known errors. These will be corrected in the next printing or next revised edition, respectively. With its emphasis, on one hand, on the basic concepts and applications rather than pure theory and mathematics, and on the other hand, coverage of quantum mechanical and classical mechanical models including examples from inorganic, organic, and biological chemistry, “Essentials” is a useful tool not only for teaching and learning but also as a quick reference, and thus will most probably become one of the standard text books for computational chemistry.

814 citations

01 Jan 2013

801 citations

Journal ArticleDOI
TL;DR: Variable neighbourhood search (VNS) is a metaheuristic, or a framework for building heuristics, based upon systematic changes of neighbourhoods both in descent phase, to find a local minimum, and in perturbation phase to emerge from the corresponding valley as mentioned in this paper.
Abstract: Variable neighbourhood search (VNS) is a metaheuristic, or a framework for building heuristics, based upon systematic changes of neighbourhoods both in descent phase, to find a local minimum, and in perturbation phase to emerge from the corresponding valley It was first proposed in 1997 and has since then rapidly developed both in its methods and its applications In the present paper, these two aspects are thoroughly reviewed and an extensive bibliography is provided Moreover, one section is devoted to newcomers It consists of steps for developing a heuristic for any particular problem Those steps are common to the implementation of other metaheuristics

718 citations