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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月22日

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  • László Mihály Vas,
  • Csenge Tóth,
  • Roland Petrény,
  • Ábris Dávid Virág

摘要

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.