arXiv subjects

Thomas M. Truskett

Publications and source records attributed to Thomas M. Truskett.

At least 19 recordsLinked to original sources

arXiv2026

Simulation and Network Assembly Pipelines for Dynamically Bonded Soft Materials

Soft materials linked by reversible covalent or supramolecular bonds form a diverse class of assemblies with promising applications from nanoscience to medicine. Experiments typically probe bulk phase behavior and rheology, but it remains difficult to measure how microscopic bonding kinetics and the mechanics of the constituent elements give rise to bulk properties. Coarse-grained molecular dynamics (MD) simulations can bridge these scales, but most simulation approaches do not control individual bond kinetics, and those that do were mostly developed for bespoke applications that do not readily generalize. Here we present pySNAP (Simulation and Network Assembly Pipelines), a modular open source Python platform that integrates tunable dynamic bonding with a workflow, template, and analysis setup, so that users can study a wide range of systems with only small changes to input files. The platform is built on the GPU-accelerated HOOMD-blue MD engine and integrates DyBond, a GPU-accelerated plugin that forms and breaks bonds consistent with an equilibrium distribution and supports bonding between multiple types of partner species. Around this core, the snap_simulate package compiles a directory of parameter files into a HOOMD-blue simulation, and the snap_workflow package orchestrates the resulting parameter sweeps across workstations and high-performance computing schedulers. We describe the theory behind simulated dynamic bonding and how to use the package, from setting up a parameter sweep to analyzing its results, and demonstrate the framework on a diverse range of dynamically bonded systems, showing that it accommodates distinct interaction mechanisms, geometries, and physical scenarios within a unified workflow, while enabling both reproduction of existing models and rapid construction of more complex composite systems.

cond-mat.soft↗

arXiv2026

Coarse-graining to create minimalist models for dynamic, end-linked star-polymer networks

Although minimalist models for patchy attractive particles have revealed powerful design rules for how particle valence directs colloidal assembly, less work has focused on comparably simple models of reversible, network-forming star polymers. Here, we use relative-entropy coarse-graining and simulation results from finer-resolution bead--spring star polymers to generate 5-bead models of dynamic, end-associating four-armed poly(ethylene glycol) macromers. Our results comparing structural correlations, network connectivity, and phase behavior of the coarse- and fine-grained models provide insight into how the accuracy and transferability of the coarse-grained models depend on the state point chosen for coarse-graining. The results also reveal intrinsic trade-offs between reducing degrees of freedom and expanding the range of effective interactions in coarse-graining that impact the total number of pairwise interactions in the resulting model, with implications for its computational efficiency.

cond-mat.soft↗

arXiv2026

Characterizing emergent multiscale dynamics in colloidal nanoparticle gels

Colloidal gels assembled from nanoparticles (NPs) are a versatile class of soft network-based materials capable of rich dynamic, mechanical, and even optical or magnetic responses to stimuli. Understanding how their hierarchically organized processes relate to macroscopic network properties remains a broad and unresolved problem in soft matter physics. The mechanisms of gel formation can depend sensitively on the pathway and the nature of NP interactions, thus far preventing a unified theoretical bridge between nanoscopic interactions and structural evolution and network dynamics. Indirect measurement of dynamics using light-scattering techniques provides an experimental means to quantify underlying particle and network motion. X-ray photon correlation spectroscopy (XPCS) has emerged as a powerful tool for probing nanoscopic motion in nanoparticle gels, but alone cannot resolve the full spatiotemporal spectrum of dynamics that drive gelation, aging, and network mechanical properties. While in situ rheo-XPCS enables simultaneous probing of nanoscale and bulk mechanical responses, complementary light scattering, microscopy, or simulations can extend spatiotemporal characterization and, consequently, understanding of NP gel network physics. Implementing a modular model platform with tunable primary nanoparticle features allows systematic variation of nanoscopic characteristics that drive emergent gel responses and inform the development of theoretical models for a wide range of soft, dynamic, nanostructured materials. The rapid expansion of XPCS capabilities at fourth-generation light sources, combined with complementary tools and robust model systems, positions the field to move beyond descriptive fundamental studies toward the design of nanoparticle gels with adaptive and programmable behaviors.

cond-mat.soft↗

arXiv2025

Simulating dynamic bonding in soft materials

Dynamic bonding is an essential feature of many soft materials. Molecular simulations have proven to be a powerful tool for modeling bonding kinetics and thermodynamics in these materials, providing insights into their properties that cannot be obtained by experiments alone. Here, we review recent advances in modeling dynamic bonding in soft matter via molecular dynamics, Monte Carlo, and hybrid simulation methods, highlighting outstanding challenges and future directions.

cond-mat.soft↗

arXiv2025

Plasmon Polaritons in Disordered Nanoparticle Assemblies

Multilayer assemblies of metal nanoparticles can act as photonic structures, where collective plasmon resonances hybridize with cavity modes to create plasmon-polariton states. For sufficiently strong coupling, plasmon polaritons qualitatively alter the optical properties of light-matter systems, with applications ranging from sensing to solar energy. However, results from experimental studies have raised questions about the role of nanoparticle structural disorder in plasmon-polariton formation and light-matter coupling in plasmonic assemblies. Understanding how disorder affects optical properties has practical implications since methods for assembling low-defect nanoparticle superlattices are slow and scale poorly. Modeling realistic disorder requires large system sizes, which is challenging using conventional electromagnetic simulations. We employ Brownian dynamics simulations to construct large-scale nanoparticle multilayers with controlled structural order. We investigate their optical response using a superposition T-matrix method with 2-D periodic boundary conditions. We find that while structural disorder broadens the polaritonic stop band and the near-field hot-spot distribution, the polariton dispersion and coupling strength remain unaltered. To understand effects of nanoparticle composition, we consider assemblies with model particles mimicking gold or tin-doped indium oxide (ITO) nanocrystals. Losses due to higher damping in ITO nanocrystals prevent their assemblies from achieving the deep strong coupling of gold nanoparticle multilayers, although the former still exhibit ultrastrong coupling. We demonstrate that while computationally efficient mutual polarization method calculations employing the quasistatic approximation modestly overestimate the strength of the collective plasmon, they reproduce the polariton dispersion relations determined by electrodynamic simulations.

physics.optics↗

arXiv2024

Plasmonic Metal Oxide Nanocrystals as Building Blocks for Infrared Metasurfaces

Based on experimental and simulation methods we helped develop, we are advancing mechanistic understanding of how self-assembled NC metamaterials can produce distinctive near- and far-field optical properties not readily achievable in lithographically patterned structures. First, the impacts of the inevitable defects and disorder associated with self-assembly can be rationalized and, in some cases, recognized as advantageous. Second, self-assembly enables intimate nanoscale intermixing of different NC and molecular components. By incorporating probe molecules within the gaps between NCs where the electric field enhancement is strongest, we show enhanced detection of molecular vibrations that can be optimized by tuning the size and resonance frequency of the NCs. We show how metasurfaces incorporating mixtures of NCs with different doping concentrations can achieve epsilon-near-zero dielectric response over a broad frequency range. Finally, considering the NC metasurface itself as a building block, we show how photonic structures incorporating these assemblies can harness and amplify their distinctive properties. Through modeling the NC monolayer as a slab with an effective permittivity response, we designed a frequency-tunable IR perfect absorber by layering the NCs on a simple open cavity structure. Since the perfect absorption architecture further enhances the IR electric field localization strength, we expect that this integration strategy can enhance molecular vibration coupling or non-linear optical response. The versatility of the NC assembly and integration approach suggests opportunities for various metal oxide NC superstructures, including mixing and stacking of NCs beyond a single monolayer, representing a vast parameter space for design of linear and nonlinear IR optical components.

physics.optics↗

arXiv2023

Morphological Control of Bundled Actin Networks Subject to Fixed-Mass Depletion

Depletion interactions are thought to significantly contribute to the organization of intracellular structures in the crowded cytosol. The strength of depletion interactions depends on physical parameters like the depletant number density and the depletant size ratio. Cells are known to dynamically regulate these two parameters by varying the copy number of proteins of a wide distribution of sizes. However, mammalian cells are also known to keep the total protein mass density remarkably constant, to within 0.5% throughout the cell cycle. We thus ask how the strength of depletion interactions varies when the total depletant mass is held fixed, a.k.a. fixed-mass depletion. We answer this question via scaling arguments, as well as by studying depletion effects on networks of reconstituted semiflexible actin $\textit{in silico}$ and $\textit{in vitro}$. We examine the maximum strength of the depletion interaction potential $U^*$ as a function of $q$, the size ratio between the depletant and the matter being depleted. We uncover a scaling relation $U^* \sim q^{-ζ}$ for two cases: fixed volume fraction $ϕ$ and fixed mass density $ρ$. For fixed volume fraction, we report $ζ< 0$. For the fixed mass density case, we report $ζ> 0$, which suggests the depletion interaction strength increases as the depletant size ratio is increased. To test this prediction, we prepared our filament networks at fixed mass concentrations with varying sizes of the depletant molecule poly(ethylene glycol) (PEG). We characterize the depletion interaction strength in our simulations via the mesh size. In experiments, we observe two distinct actin network morphologies, which we call weakly bundled and strongly bundled. We identify a mass concentration where different PEG depletant sizes leads to weakly bundled or strongly bundled morphologies...[more in main text].

cond-mat.soft↗

arXiv2022

Depletion-Driven Morphological Control of Bundled Actin Networks

The actin cytoskeleton is a semiflexible biopolymer network whose morphology is controlled by a wide range of biochemical and physical factors. Actin is known to undergo a phase transition from a single-filament state to a bundled state by the addition of polyethylene glycol (PEG) molecules in sufficient concentration. While the depletion interaction experienced by these biopolymers is well-known, the effect of changing the molecular weight of the depletant is less well understood. Here, we experimentally identify a phase transition in solutions of actin from networks of filaments to networks of bundles by varying the molecular weight of PEG polymers, while holding the concentration of these PEG polymers constant. We examine the states straddling the phase transition in terms of micro and macroscale properties. We find that the mesh size, bundle diameter, persistence length, and intra-bundle spacing between filaments across the line of criticality do not show significant differences, while the relaxation time, storage modulus, and degree of bundling change between the two states do show significant differences. Our results demonstrate the ability to tune actin network morphology and mechanics by controlling depletant size, a property which could be exploited to develop actin-based materials with switchable rigidity.

cond-mat.soft↗

arXiv2022

Multiscale modeling of solute diffusion in triblock copolymer membranes

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

cond-mat.soft↗

arXiv2022

Dynamics of equilibrium linked colloidal gels

Colloids that attractively bond to only a few neighbors (e.g., patchy particles) can form equilibrium gels with distinctive dynamic properties that are stable in time. Here, we use a coarse-grained model to explore the dynamics of linked networks of patchy colloids whose average valence is macroscopically, rather than microscopically, constrained. Simulation results for the model show dynamic hallmarks of equilibrium gel formation and establish that the colloid-colloid bond persistence time controls the characteristic slow relaxation of the self-intermediate scattering function. The model features re-entrant network formation without phase separation as a function of linker concentration, centered at the stoichiometric ratio of linker ends to nanoparticle surface bonding sites. Departures from stoichiometry result in linker-starved or site-starved networks with reduced connectivity and shorter characteristic relaxation times with lower activation energies. Underlying the re-entrant trends, dynamic properties vary monotonically with the number of effective network bonds per colloid, a quantity that can be predicted using Wertheim's thermodynamic perturbation theory. These behaviors suggest macroscopic in situ strategies for tuning the dynamical response of colloidal networks.

cond-mat.soft↗

arXiv2022

Treating random sequential addition via the replica method

While many physical processes are non-equilibrium in nature, the theory and modeling of such phenomena lag behind theoretical treatments of equilibrium systems. The diversity of powerful theoretical tools available to describe equilibrium systems has inspired strategies that map non-equilibrium systems onto equivalent equilibrium analogs so that interrogation with standard statistical mechanical approaches is possible. In this work, we revisit the mapping from the non-equilibrium random sequential addition process onto an equilibrium multi-component mixture via the replica method, allowing for theoretical predictions of non-equilibrium structural quantities. We validate the above approach by comparing the theoretical predictions to numerical simulations of random sequential addition.

cond-mat.stat-mech↗

arXiv2022

Geometric model of crack-templated networks for transparent conductive films

Crack-templated networks, metallic frameworks fabricated from crack patterns in sacrificial thin films, can exhibit high optical transmittance, high electric conductivity, and a host of other properties attractive for applications. Despite advances in preparing, characterizing, and analyzing optoelectronic performance of cracked template networks, limited efforts have focused on predicting how their disordered structures help determine their electrical and optical properties and explain their interrelationships. We introduce a geometric modeling approach for crack-templated networks and use simulation to compute their wavelength- and incident angle-dependent optical transmittance and sheet resistivity. We explore how these properties relate to one another and to those of metallic meshes with periodically ordered aperture arrays. We consider implications of the results for optoelectronic applications, compare figure-of-merit predictions to experimental data, and highlight an opportunity to extend the modeling approach using inverse methods.

physics.app-ph↗

arXiv2022

Machine learning-assisted design of material properties

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties.

cond-mat.mtrl-sci↗

arXiv2021

Assembling Inorganic Nanocrystal Gels

Inorganic nanocrystal gels retain distinct properties of individual nanocrystals while offering tunable, network structure-dependent characteristics. We review different mechanisms for assembling gels from colloidal nanocrystals including (1) controlled destabilization, (2) direct bridging, (3) depletion, as well as linking mediated by (4) coordination bonding or (5) dynamic covalent bonding, and we highlight how each impacts gel properties. These approaches use nanocrystal surface chemistry or the addition of small molecules to mediate inter-nanocrystal attractions. Each method offers advantages in terms of gel stability, reversibility, or tunability and presents new opportunities for design of reconfigurable materials and fueled assemblies.

cond-mat.mtrl-sci↗

arXiv2020

Wertheim's thermodynamic perturbation theory with double-bond association and its application to colloid-linker mixtures

We extend Wertheim's thermodynamic perturbation theory to derive the association free energy of a multicomponent mixture for which double bonds can form between any two pairs of the molecules' arbitrary number of bonding sites. This generalization reduces in limiting cases to prior theories that restrict double bonding to at most one pair of sites per molecule. We apply the new theory to an associating mixture of colloidal particles ("colloids") and flexible chain molecules ("linkers"). The linkers have two functional end groups, each of which may bond to one of several sites on the colloids. Due to their flexibility, a significant fraction of linkers can "loop" with both ends bonding to sites on the same colloid instead of bridging sites on different colloids. We use the theory to show that the fraction of linkers in loops depends sensitively on the linker end-to-end distance relative to the colloid bonding-site distance, which suggests strategies for mitigating the loop formation that may otherwise hinder linker-mediated colloidal assembly.

cond-mat.soft↗

arXiv2020

Effects of linker flexibility on phase behavior and structure of linked colloidal gels

Colloidal nanocrystal gels can be assembled using a difunctional "linker" molecule to mediate bonding between nanocrystals. The conditions for gelation and the structure of the gel are controlled macroscopically by the linker concentration and microscopically by the linker's molecular characteristics. Here, we demonstrate using a toy model for a colloid-linker mixture that linker flexibility plays a key role in determining both phase behavior and structure of the mixture. We fix the linker length and systematically vary its bending stiffness to span the flexible, semiflexible, and rigid regimes. At fixed linker concentration, flexible-linker and rigid-linker mixtures phase separate at low colloid volume fractions in agreement with predictions of first-order thermodynamic perturbation theory, but the semiflexible-linker mixtures do not. We correlate and attribute this qualitatively different behavior to undesirable "loop" linking motifs that are predicted to be more prevalent for linkers with end-to-end distances commensurate with the locations of chemical bonding sites on the colloids. Linker flexibility also influences the spacing between linked colloids, suggesting strategies to design gels with desired phase behavior, structure, and by extension, structure-dependent properties.

cond-mat.soft↗

arXiv2020

Stability of force-driven shear flows in nonequilibrium molecular simulations with periodic boundaries

We analyze the hydrodynamic stability of force-driven parallel shear flows in nonequilibrium molecular simulations with three-dimensional periodic boundary conditions. We show that flows simulated in this way can be linearly unstable, and we derive an expression for the critical Reynolds number as a function of the geometric aspect ratio of the simulation domain. Approximate periodic extensions of Couette and Poiseuille flows are unstable at Reynolds numbers two orders of magnitude smaller than their aperiodic equivalents because the periodic boundaries impose fundamentally different constraints on the flow. This instability has important implications for simulating shear rheology and for designing nonequilibrium simulation methods that are compatible with periodic boundary conditions.

physics.flu-dyn↗

arXiv2020

Inverse methods for design of soft materials

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. While multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this Perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.

cond-mat.soft↗