arXiv subjects

John J. Karnes

Publications and source records attributed to John J. Karnes.

4 recordsLinked to original sources

arXiv2026

Bridging simulation length scales with cellular automata

Multiscale simulation requires coupling physics models that operate at different characteristic length and time scales, because no single method spans the range needed for most problems of interest. Moving to a coarser-grained representation unlocks longer length and time scales, but it discards the microscopic interactions that build morphology. A fine-grained model can be initialized from an arbitrary packing and left to self-assemble into a physically meaningful structure; a lower-resolution model cannot, and must inherit its starting configuration from a higher-fidelity simulation. The length scales accessible to the coarse-grained model are therefore set not by the coarse-grained method itself, but by the largest fine-grained configuration that can be affordably equilibrated. A representative example is the scale-up from particle-based molecular dynamics (MD) to a lattice-based representation such as kinetic Monte Carlo (kMC). In this work, we present a cellular automata (CA) approach for generating arbitrarily large lattice starting configurations. CA is a natural fit for this task: short-ranged local rules drive the evolution of a lattice, and their repeated application gives rise to emergent long-range order, thematically mirroring how short-ranged interactions in MD produce self-assembled morphology. We use a configuration from a higher-fidelity simulation as a training set and learn the CA rules from it via logistic regression. As a proof of principle, we develop these rules for a hydrated anion exchange membrane (AEM), generate new starting configurations, and benchmark their performance in mesoscale kMC simulations against an MD-derived "ground truth." We then demonstrate the ability to generate substantially larger lattices and show that their behavior in kMC is consistent with that of the smaller CA benchmark configurations.

cond-mat.mtrl-sci↗

arXiv2025

On void formation during the simulated tensile testing of polymer-filler particle composites

We simulate a series of model polymer composites, composed of linear polymer strands and spherical, monodisperse filler particles (FP). These molecular dynamics simulations implement a coarse-grained, bead-spring force field and we vary several formulation parameters to study their respective influences on material properties. These parameters include FP radius, FP volume fraction, temperature, and the polymer-polymer, FP-FP, and polymer-FP interaction potentials. Uniaxial extension of the simulation cells allows direct comparison of mechanical reinforcement (or weakening) provided by the FP. We focus on the formation of microscopic voids during simulated tensile testing of glassy polymer composites and quantify how the characteristic spatial and morphological arrangement of these voids is a function of interaction potentials used in the simulation. We discuss the implications of these findings in the context of polymer composite design and formulation.

cond-mat.soft↗

arXiv2023

Particle-Based Simulations of Electrophoretic Deposition with Adaptive Physics Models

This work represents an extension of mesoscale particle-based modeling of electrophoretic deposition (EPD), which has relied exclusively on pairwise interparticle interactions described by Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. With this standard treatment, particles continuously move and interact via excluded volume and electrostatic pair potentials under the influence of external fields throughout the EPD process. The physics imposed by DLVO theory may not be appropriate to describe all systems, considering the vast material, operational, and application space available to EPD. As such, we present three modifications to standard particle-based models, each rooted in the ability to dynamically change interparticle interactions as simulated deposition progresses. This approach allows simulations to capture charge transfer and/or irreversible adsorption based on tunable parameters. We evaluate and compare simulated deposits formed under new physical assumptions, demonstrating the range of systems that these adaptive physics models may capture.

cond-mat.mes-hall↗

arXiv2022

Isolating Chemical Reaction Mechanism as a Variable with Reactive Coarse-Grained Molecular Dynamics: Step-Growth versus Chain-Growth Polymerization

We present a general approach to isolate chemical reaction mechanism as an independently controllable variable across chemically distinct systems. Modern approaches to reduce the computational expense of molecular dynamics simulations often group multiple atoms into a single "coarse-grained" interaction site, which leads to a loss of chemical resolution. In this work we convert this shortcoming into a feature and use identical coarse-grained models to represent molecules that share non-reactive characteristics but react by different mechanisms. As a proof of concept we use this approach to simulate and investigate distinct, yet similar, trifunctional isocyanurate resin formulations that polymerize by either chain- or step-growth. Since the underlying molecular mechanics of these models are identical, all emergent differences are a function of the reaction mechanism only. We find that the microscopic morphologies resemble related all-atom simulations and that simulated mechanical testing reasonably agrees with experiment.

cond-mat.mtrl-sci↗