The Role of Graph-Theoretical Invariants in Chemistry.

Abstract

Graph-theoretical invariants have come to play an increasingly important role in chemistry over the past two decades. Starting from the chemical graph representing some molecular species, the most frequently derived invariants are simple numerical descriptors and polynomials. Whereas the polynomials have been used widely in the study of problems relating to chemical bonding theory, the numerical invariants have found major application in the prediction of the behavior of chemical species. The numerical descriptors, known to chemists as topological indices, are treated as inherent properties of the molecules they are employed to characterize. As such, they can be correlated against many other, experimentally measured properties of the molecules. It is from correlations of this type that predictions of the properties of unmeasured species can be made. Topological indices enjoy the twin advantages of being comparatively easy to compute and of yielding a result which is free from (experimental) error. To date, the molecular properties which topological indices have been advanced in the chemical literature, though only a handful have so far found significant application. The results of correlative studies have in general proved to be highly encouraging, and lead us to the conclusion that the use of topological indices represents a significant advance in the prediction of the behavior of chemical substances. Keywords: Graph theory, Invariants, Topolical indices.

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Document Details

Document Type
Technical Report
Publication Date
Mar 06, 1987
Accession Number
ADA178063

Entities

People

  • D. H. Rouvray

Organizations

  • University of Georgia

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Aromatic Hydrocarbons
  • Biological Sciences
  • Boiling
  • Boiling Point
  • Chemistry
  • Coefficients
  • Combustion
  • Crystal Lattices
  • Cyclic Hydrocarbons
  • Equations
  • Heat Energy
  • Heat Of Combustion
  • Hydrocarbons
  • Internal Combustion Engines
  • Polynomials
  • Regression Analysis

Readers

  • Graph Algorithms and Convex Optimization.
  • Organic Chemistry
  • Regression Analysis.