Abstract
Abstract
We present a molecular-dynamics (MD)-in-the-loop multi-objective optimization framework for the design of polymer/clay interfaces, and apply it to poly(vinyl alcohol) (PVA) reinforced with sodium montmorillonite (Na-MMT), an inexpensive commodity polymer whose value can be raised substantially by nanoclay reinforcement for demanding applications such as tissue-engineering scaffolds. The key methodological advance is to treat each expensive all-atom MD evaluation as the objective function of a surrogate-assisted Bayesian optimization loop based on q-expected hypervolume improvement (qEHVI), so that competing interfacial design objectives can be optimized with an order of magnitude fewer simulations than population-based methods require. To provide the physical basis for this loop, an equilibrated PVA/Na-MMT interface (9074 atoms) was first characterized, combining a CHARMM-compatible force field for atactic PVA with the INTERFACE force field for the clay: PVA adsorbs strongly onto the Na-MMT surface with a work of adhesion of ∼446 mJ/m 2 (−1472 kcal/mol), forming a densified adsorption layer (+28% vs bulk) dominated (∼95%) by a dense network of ∼34 PVA(-OH)…clay(O) hydrogen bonds, and the MD glass-transition temperature (∼330–350 K) is consistent with experiment (∼358 K). Using clay loading as the design variable, the qEHVI loop then maps the full Pareto front of the strongly conflicting adhesion and matrix-flexibility objectives in only seven real MD evaluations, identifying a balanced-design composition at intermediate clay loading, and is shown to reach a given hypervolume with roughly half the evaluations of random search. The framework reframes interfacial MD from one-off characterization into a design-oriented workflow, providing a transferable, simulation-based route to the rational optimization of polymer/clay composites.
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@article{Ko2026Interfacial,
title = {Interfacial adhesion, hydrogen bonding and thermal behavior of poly(vinyl alcohol)/sodium montmorillonite composites: A molecular dynamics study with a surrogate-assisted optimization framework},
author = {Muhammed Zahid Koç},
journal = {Computational Materials Science},
year = {2026},
doi = {10.1016/j.commatsci.2026.115124},
url = {https://doi.org/10.1016/j.commatsci.2026.115124}
}
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