The Geometry of the Hückel/Tight-Binding Method for Modeling Molecular Systems

Abstract. We present a geometric reformulation of the Hückel molecular orbital/tight-binding (HMO/TB) method that integrates tools from algebraic graph theory and statistical mechanics. By extending classical notions of charge density and bond order to incorporate bond orbital energies, we construct a probabilistic framework that embeds molecular graphs into Euclidean (pseudometric) space. This embedding introduces new geometric descriptors—such as internodal distances, node angles, and electronic propagation efficiency—that correlate with chemical properties including bond lengths, reactivity, and molecular stability. Analytical results for linear and cyclic polyenes as well as polycyclic aromatic hydrocarbons illustrate the utility of these descriptors and suggest a pathway toward closer integration of HMO/TB with ab initio methods and machine learning approaches in molecular modeling.

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Publication Details

Journal
SIAM Journal on Applied Mathematics
Published
2026-07-24
DOI
https://doi.org/10.1137/25m1791883
Primary Topic
Graph theory and applications
Type
article
Field-Weighted Citation Impact
0.00

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article

The Geometry of the Hückel/Tight-Binding Method for Modeling Molecular Systems

Ernesto Estrada, Marisol Bermúdez‐Montaña
SIAM Journal on Applied Mathematics
Graph theory and applications
article

The Geometry of the Hückel/Tight-Binding Method for Modeling Molecular Systems

Ernesto Estrada, Marisol Bermúdez‐Montaña
article en

Abstract

Abstract. We present a geometric reformulation of the Hückel molecular orbital/tight-binding (HMO/TB) method that integrates tools from algebraic graph theory and statistical mechanics. By extending classical notions of charge density and bond order to incorporate bond orbital energies, we construct a probabilistic framework that embeds molecular graphs into Euclidean (pseudometric) space. This embedding introduces new geometric descriptors—such as internodal distances, node angles, and electronic propagation efficiency—that correlate with chemical properties including bond lengths, reactivity, and molecular stability. Analytical results for linear and cyclic polyenes as well as polycyclic aromatic hydrocarbons illustrate the utility of these descriptors and suggest a pathway toward closer integration of HMO/TB with ab initio methods and machine learning approaches in molecular modeling.

SIAM Journal on Applied MathematicsVol. 86(4)
Institute for Cross-Disciplinary Physics and Complex Systems (ES), Tecnológico de Monterrey (MX)
European Regional Development Fund, Agencia Estatal de Investigación
Openalex Percentile: Top 4%
Graph theory and applications
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