Stochastic modeling framework for polymer bonding based on classical physics and chemical bonding model
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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.
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Research context
OpenAlex topics: Computational Drug Discovery Methods · Polymer crystallization and properties · Machine Learning in Materials Science
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References (43)
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