Stochastic modeling framework for polymer bonding based on classical physics and chemical bonding model

Abstract In this article, we present a unified stochastic framework that models both primary (covalent) and secondary (noncovalent) bonding in polymers by mapping electron-residence probabilities onto classical potentials. From simple probabilistic assumptions together with Coulombic and a modified Lennard–Jones functional form, we derive closed-form, distance-dependent expressions for bond energies and forces and space correlation function that are directly usable in atomistic simulations. Validation on O–H interactions in polylactic-acid hydrolysis yields excellent agreement with detailed force-field calculations at a small fraction of the computational cost. The analytic, low-parameter nature of the approach facilitates rapid incorporation of physics-based bond descriptions into kinetic, continuum, and multiscale polymer modeling workflows and is naturally extensible with lightweight data-driven corrections.

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Publication Details

Journal
Applied Physics A
Published
2025-10-22
DOI
https://doi.org/10.1007/s00339-025-08971-4
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Stochastic modeling framework for polymer bonding based on classical physics and chemical bonding model

László Mihály Vas, Csenge Tóth, Roland Petrény, Ábris Dávid Virág
Applied Physics A
Computational Drug Discovery Methods
article

Stochastic modeling framework for polymer bonding based on classical physics and chemical bonding model

László Mihály Vas, Csenge Tóth, Roland Petrény, Ábris Dávid Virág
article en

Abstract

Abstract In this article, we present a unified stochastic framework that models both primary (covalent) and secondary (noncovalent) bonding in polymers by mapping electron-residence probabilities onto classical potentials. From simple probabilistic assumptions together with Coulombic and a modified Lennard–Jones functional form, we derive closed-form, distance-dependent expressions for bond energies and forces and space correlation function that are directly usable in atomistic simulations. Validation on O–H interactions in polylactic-acid hydrolysis yields excellent agreement with detailed force-field calculations at a small fraction of the computational cost. The analytic, low-parameter nature of the approach facilitates rapid incorporation of physics-based bond descriptions into kinetic, continuum, and multiscale polymer modeling workflows and is naturally extensible with lightweight data-driven corrections.

Applied Physics AVol. 131(11)
Budapest University of Technology and Economics (HU)
Budapesti Műszaki és Gazdaságtudományi Egyetem, Nemzeti Kutatási, Fejlesztési és Innovaciós Alap
Openalex Percentile: Top 68%
Computational Drug Discovery Methods
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