Multiscale Molecular Simulations of Polymer-Matrix Nanocomposites: or What Molecular Simulations Have Taught us About the Fascinating Nanoworld

From Linear to Nets: Multiconfiguration Polymer Structure Generation with PolyFlin

Molecular modeling and simulations are essential tools in polymer science and engineering, enabling researchers to predict and understand the properties of macromolecules, including their structure, dynamics, thermodynamics, and overall material characteristics. However, one of the key challenges in polymer simulation and modeling lies in the initial topology design, as existing programs often lack the capability to generate all types of polymer forms. In this study, we present PolyFlin, a powerful Python module that addresses this limitation by allowing the generation of a wide range of polymer structures, from simple homopolymers to complex copolymers, including grafts, cyclic, star, dendrimers, and nets. PolyFlin offers a versatile and efficient tool for exploring and creating diverse polymer architectures, facilitating advancements in various fields that require precise polymer modeling and simulation.

Coarse-Grained Modeling Using Neural Networks Trained on Structural Data

We propose a method of bottom-up coarse-graining, in which interactions within a coarse-grained model are determined by an artificial neural network trained on structural data obtained from multiple atomistic simulations. The method uses ideas of the inverse Monte Carlo approach, relating changes in the neural network weights with changes in average structural properties, such as radial distribution functions. As a proof of concept, we demonstrate the method on a system interacting by a Lennard-Jones potential modeled by a simple linear network and a single-site coarse-grained model of methanol-water solutions. In the latter case, we implement a nonlinear neural network with intermediate layers trained by atomistic simulations carried out at different methanol concentrations. We show that such a network acts as a transferable potential at the coarse-grained resolution for a wide range of methanol concentrations, including those not included in the training set.

Interdependence of Surface Roughness on Icephobic Performance: A Review

Ice protection techniques have attracted significant interest, notably in aerospace and wind energy applications. However, the current solutions are mostly costly and inconvenient due to energy-intensive and environmental concerns. One of the appealing strategies is the use of passive icephobicity, in the form of coatings, which is induced by means of several material strategies, such as hydrophobicity, surface texturing, surface elasticity, and the physical infusion of ice-depressing liquids, etc. In this review, surface-roughness-related icephobicity is critically discussed to understand the challenges and the role of roughness, especially on superhydrophobic surfaces. Surface roughness as an intrinsic, independent surface property for anti-icing and de-icing performance is also debated, and their interdependence is explained using the related physical mechanisms and thermodynamics of ice nucleation. Furthermore, the role of surface roughness in the case of elastomeric or low-modulus polymeric coatings, which typically instigate an easy release of ice, is examined. In addition to material-centric approaches, the influence of surface roughness in de-icing evaluation is also explored, and a comparative assessment is conducted to understand the testing sensitivity to various surface characteristics. This review exemplifies that surface roughness plays a crucial role in incorporating and maintaining icephobic performance and is intrinsically interlinked with other surface-induced icephobicity strategies, including superhydrophobicity and elastomeric surfaces. Furthermore, the de-icing evaluation methods also appear to be roughness sensitive in a certain range, indicating a dominant role of mechanically interlocked ice.

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

Polymer solution structure and dynamics within pores of hexagonally close-packed nanoparticles

Using coarse-grained molecular dynamics simulations, we examine structure and dynamics of polymer solutions under confinement within the pores of a hexagonally close-packed (HCP) nanoparticle system with nanoparticle diameter fifty times that of the polymer Kuhn segment size. We model a condition where the polymer chain is in a good solvent (i.e., polymer-polymer interaction is purely repulsive and polymer-solvent and solvent-solvent interactions are attractive) and the polymer-nanoparticle and solvent-nanoparticle interactions are purely repulsive. We probe three polymer lengths (N = 10, 114, and 228 Kuhn segments) and three solution concentrations (1, 10, and 25%v) to understand how the polymer chain conformations and chain center-of-mass diffusion change under confinement within the pores of the HCP nanoparticle structure from those seen in bulk. The known trend of bulk polymer R_g² decreasing with increasing concentration no longer holds when confined in the pores of HCP nanoparticle structure; for example, for the 114-mer, the HCP 〈R_g²〉 at 1%v concentration is lower than HCP 〈R_g²〉 at 10%v concentration. The 〈R_g²〉 of the 114-mer and 228-mer exhibit the largest percent decline going from bulk to HCP at the 1%v concentration and the smallest percent decline at the 25%v concentration. We also provide insight into how the confinement ratio (CR) of polymer chain size to pore size within tetrahedral and octahedral pores in the HCP arrangement of nanoparticles affects the chain conformation and diffusion at various concentrations. At the same concentration, the N = 114 has significantly more movement between pores than the N = 228 chains. For the N = 114 polymer, the diffusion between pores (i.e., inter-pore diffusion) accelerates the overall diffusion rate for the confined HCP system while for the N = 228 polymer, the polymer diffusion in the entire HCP is dominated by the diffusion within the tetrahedral or octahedral pores with minor contributions from inter-pore diffusion. These findings augment the fundamental understanding of macromolecular diffusion through large, densely packed nanoparticle assemblies and are relevant to research focused on fabrication of polymer composite materials for chemical separations, storage, optics, and photonics. We perform coarse-grained molecular dynamics simulations to understand structure and dynamics of polymer solutions under confinement within hexagonal close packed nanoparticles with radii much larger than the polymer chain's bulk radius of gyration.

Dynamics of Polymer Chains in Poly(ethylene oxide)/Silica Nanocomposites via a Combined Computational and Experimental Approach

The dynamics of polymer chains in poly(ethylene oxide)/silica (PEO/SiO₂) nanoparticle nanohybrids have been investigated via a combined computational and experimental approach involving atomistic molecular dynamics simulations and dielectric relaxation spectroscopy (DRS) measurements. The complementarity of the approaches allows us to study systems with different polymer molecular weights, nanoparticle radii, and compositions across a broad range of temperatures. We study the effects of spatial confinement, which is induced by the nanoparticles, and chain adsorption on the polymer's structure and dynamics. The investigation of the static properties of the nanocomposites via detailed atomistic simulations revealed a heterogeneous polymer density layer at the vicinity of the PEO/SiO₂ interface that exhibited an intense maximum close to the inorganic surface, whereas the bulk density was reached for distances ∼1-1.2 nm away from the nanoparticle. For small volume fractions of nanoparticles, the polymer dynamics, probed by the atomistic simulations of low-molecular-weight chains at high temperatures, are consistent with the presence of a thin adsorbed layer that exhibits slow dynamics, with the dynamics far away from the nanoparticle being similar to those in the bulk. However, for high volume fractions of nanoparticles (strong confinement), the dynamics of all polymer chains were predicted slower than that in the bulk. On the other hand, similar dynamics were found experimentally for both the local β-process and the segmental dynamics for high-molecular-weight systems measured at temperatures below the melting temperature of the polymer, which were probed by DRS. These differences can be attributed to various parameters, including systems of different molecular weights and nanoparticle states of dispersion, the different temperature range studied by the different methods, the potential presence of a reduced-mobility PEO/SiO₂ interfacial layer that does not contribute to the dielectric spectrum, and the presence of amorphous-crystalline interfaces in the experimental samples that may lead to a different dynamical behaviors of the PEO chains.

Continuous Manufacturing and Molecular Modeling of Pharmaceutical Amorphous Solid Dispersions

Amorphous solid dispersions enhance solubility and oral bioavailability of poorly water-soluble drugs. The escalating number of drugs with poor aqueous solubility, poor dissolution, and poor oral bioavailability is an unresolved problem that requires adequate interventions. This review article highlights recent solubility and bioavailability enhancement advances using amorphous solid dispersions (ASDs). The review also highlights the mechanism of enhanced dissolution and the challenges faced by ASD-based products, such as stability and scale-up. The role of process analytical technology (PAT) supporting continuous manufacturing is highlighted. Accurately predicting interactions between the drug and polymeric carrier requires long experimental screening methods, and this is a space where computational tools hold significant potential. Recent advancements in data science, computational tools, and easy access to high-end computation power are set to accelerate ASD-based research. Hence, particular emphasis has been given to molecular modeling techniques that can address some of the unsolved questions related to ASDs. With the advancement in PAT tools and artificial intelligence, there is an increasing interest in the continuous manufacturing of pharmaceuticals. ASDs are a suitable option for continuous manufacturing, as production of a drug product from an ASD by direct compression is a reality, where the addition of multiple excipients is easy to avoid. Significant attention is necessary for ongoing clinical studies based on ASDs, which is paving the way for the approval of many new ASDs and their introduction into the market.

Ionic Polymer Nanocomposites Subjected to Uniaxial Extension: A Nonequilibrium Molecular Dynamics Study

We explore the behavior of coarse-grained ionic polymer nanocomposites (IPNCs) under uniaxial extension up to 800% strain by means of nonequilibrium molecular dynamics simulations. We observe a simultaneous increase of stiffness and toughness of the IPNCs upon increasing the engineering strain rate, in agreement with experimental observations. We reveal that the excellent toughness of the IPNCs originates from the electrostatic interaction between polymers and nanoparticles, and that it is not due to the mobility of the nanoparticles or the presence of polymer-polymer entanglements. During the extension, and depending on the nanoparticle volume fraction, polymer-nanoparticle ionic crosslinks are suppressed with the increase of strain rate and electrostatic strength, while the mean pore radius increases with strain rate and is altered by the nanoparticle volume fraction and electrostatic strength. At relatively low strain rates, IPNCs containing an entangled matrix exhibit self-strengthening behavior. We provide microscopic insight into the structural, conformational properties and crosslinks of IPNCs, also referred to as polymer nanocomposite electrolytes, accompanying their unusual mechanical behavior.

Structure and Dynamics of Highly Attractive Polymer Nanocomposites in the Semi-Dilute Regime: The Role of Interfacial Domains and Bridging Chains

Detailed molecular dynamics (MD) simulations are employed to study how the presence of adsorbed domains and nanoparticle bridging chains affect the structural, conformational, thermodynamic, and dynamic properties of attractive polymer nanocomposite melts in the semi-dilute regime. As a model system we have chosen an unentangled poly(ethylene glycol) (PEG) matrix containing amorphous spherical silica nanoparticles with different diameters and at different concentrations. Emphasis is placed on properties such as the polymer mass density profile around nanoparticles, the compressibility of the system, the mean squared end-to-end distance of PEG chains, their orientational and diffusive dynamics, the single chain form factor, and the scattering functions. Our analysis reveals a significant impact of the adsorbed, interfacial polymer on the microscopic dynamic and conformational properties of the nanocomposite, especially under conditions favoring higher surface-to-volume ratios (e.g., for small nanoparticle sizes at fixed nanoparticle loading, or for higher silica concentrations). Simultaneously, adsorbed polymer chains adopt graft-like conformations, a feature that allows them to considerably extend away from the nanoparticle surface to form bridges with other nanoparticles. These bridges drive the formation of a nanoparticle network whose strength (number of tie chains per nanoparticle) increases substantially with increasing concentration of the polymer matrix in nanoparticles, or with decreasing nanoparticle size at fixed nanoparticle concentration. The presence of hydroxyl groups at the ends of PEG chains plays a key role in the formation of the network. If hydroxyl groups are substituted by methoxy ones, the simulations reveal that the number of bridging chains per nanoparticle decreases dramatically, thus the network formed is less dense and less strong mechanically, and has a smaller impact on the properties of the nanocomposite. Our simulations predict further that the isothermal compressibility and thermal expansion coefficient of PEG-silica nanocomposites are significantly lower than those of pure PEG, with their values decreasing practically linear with increasing concentration of the nanocomposite in nanoparticles.

Self-assembly in biobased nanocomposites for multifunctionality and improved performance

Concerns of petroleum dependence and environmental pollution prompt an urgent need for new sustainable approaches in developing polymeric products. Biobased polymers provide a potential solution, and biobased nanocomposites further enhance the performance and functionality of biobased polymers. Here we summarize the unique challenges and review recent progress in this field with an emphasis on self-assembly of inorganic nanoparticles. The conventional wisdom is to fully disperse nanoparticles in the polymer matrix to optimize the performance. However, self-assembly of the nanoparticles into clusters, networks, and layered structures provides an opportunity to address performance challenges and create new functionality in biobased polymers. We introduce basic assembly principles through both blending and in situ synthesis, and identify key technologies that benefit from the nanoparticle assembly in the polymer matrix. The fundamental forces and biobased polymer conformations are discussed in detail to correlate the nanoscale interactions and morphology with the macroscale properties. Different types of nanoparticles, their assembly structures and corresponding applications are surveyed. Through this review we hope to inspire the community to consider utilizing self-assembly to elevate functionality and performance of biobased materials. Development in this area sets the foundation for a new era of designing sustainable polymers in many applications including packaging, construction chemicals, adhesives, foams, coatings, personal care products, and advanced manufacturing.

Tailoring Antifouling Properties of Nanocarriers via Entropic Collision of Polymer Grafting

Polymer graftings (PGs) are widely employed in antifouling surfaces and drug delivery systems to regulate the interaction with a foreign environment. Through molecular dynamics simulations and scaling theory analysis, we investigate the physical antifouling properties of PGs via their collision behaviors. Compared with mushroom-like PGs with low grafting density, we find brush-like PGs with high grafting density could generate large deformation-induced entropic repulsive force during a collision, revealing a microscopic mechanism for the hop motions of polymer-grafted nanoparticles for drug delivery observed in experiment. In addition, the collision elasticity of PGs is found to decay with the collision velocity by a power law, i.e., a concise dynamic scaling despite the complex process involved, which is beyond expectation. These results elucidate the dynamic interacting mechanism of PGs, which are of immediate interest for a fundamental understanding of the antifouling performance of PGs and the rational design of PG-coated nanoparticles in nanomedicine for drug delivery.

Assembly of Polymer-Grafted Nanoparticles in Polymer Matrices

Leibler pioneered the idea that long enough matrix polymers of length P will spontaneously dewet a chemically identical polymer layer, comprising chains of length N, densely end-grafted to a flat surface ("brush"). This entropically driven idea is routinely used to explain experiments in which 10-20 nm diameter nanoparticles (NPs) densely grafted with polymer chains are found to phase separate from chemically identical melts for P/N ≳4. At lower grafting densities, these effects are also thought to underpin the self-assembly of grafted NPs into a variety of structures. To explore the validity of this picture, we conducted large-scale molecular dynamics simulations of grafted NPs in a chemically identical polymer melt. For the NPs we consider, in the ≈5 nm diameter range, we find no phase separation even for P/N = 10 in the absence of attractions. Instead, we find behavior that more closely parallels experiments when all of the chain monomers are equally attractive to each other but repel the NPs. Our results thus imply that experimental situations investigated to date are dominated by the surfactancy of the NPs, which is driven by the chemical mismatch between the inorganic core and the organic ligands (the graft and free chains are chemically identical). Entropic effects, that is, the translational entropy of the NPs and the matrix, the entropy of mixing of the grafts and the matrix, and the conformational entropy of the chains appear to thus have a second-order effect even in the context of these model systems.

Molecular Dynamics Simulation of Polyacrylamide Adsorption on Cellulose Nanocrystals

Classical molecular dynamics simulations of polyacrylamide (PAM) adsorption on cellulose nanocrystals (CNC) in a vacuum and a water environment are carried out to interpret the mechanism of the polymer interactions with CNC. The structural behavior of PAM is studied in terms of the radius of gyration, atom–atom radial distribution functions, and number of hydrogen bonds. The structural and dynamical characteristics of the polymer adsorption are investigated. It is established that in water the polymer macromolecules are mainly adsorbed in the form of a coil onto the CNC facets. It is found out that water and PAM sorption on CNC is a competitive process, and water weakens the interaction between the polymer and CNC.

Extensive CGMD Simulations of Atactic PS Providing Pseudo Experimental Data to Calibrate Nonlinear Inelastic Continuum Mechanical Constitutive Laws

In this contribution, we present a characterization methodology to obtain pseudo experimental deformation data from CG MD simulations of polymers as an inevitable prerequisite to choose and calibrate continuum mechanical constitutive laws. Without restriction of generality, we employ a well established CG model of atactic polystyrene as exemplary model system and simulate its mechanical behavior under various uniaxial tension and compression load cases. To demonstrate the applicability of the obtained data, we exemplarily calibrate a viscoelastic continuum mechanical constitutive law. We conclude our contribution by a thorough discussion of the findings obtained in the numerical pseudo experiments and give an outline of subsequent research activities. Thus, this work contributes to the field of multiscale simulation methods and adds a specific application to the body of knowledge of CG MD simulations.

Molecular Modeling Investigations of Sorption and Diffusion of Small Molecules in Glassy Polymers

With a wide range of applications, from energy and environmental engineering, such as in gas separations and water purification, to biomedical engineering and packaging, glassy polymeric materials remain in the core of novel membrane and state-of the art barrier technologies. This review focuses on molecular simulation methodologies implemented for the study of sorption and diffusion of small molecules in dense glassy polymeric systems. Basic concepts are introduced and systematic methods for the generation of realistic polymer configurations are briefly presented. Challenges related to the long length and time scale phenomena that govern the permeation process in the glassy polymer matrix are described and molecular simulation approaches developed to address the multiscale problem at hand are discussed.

Modelling Mixed-Gas Sorption in Glassy Polymers for CO₂ Removal: A Sensitivity Analysis of the Dual Mode Sorption Model

In an effort to reduce the experimental tests required to characterize the mixed-gas solubility and solubility-selectivity of materials for membrane separation processes, there is a need for reliable models which involve a minimum number of adjustable parameters. In this work, the ability of the Dual Mode Sorption (DMS) model to represent the sorption of CO₂/CH₄ mixtures in three high free volume glassy polymers, poly(trimethylsilyl propyne) (PTMSP), the first reported polymer of intrinsic microporosity (PIM-1) and tetrazole-modified PIM-1 (TZ-PIM), was tested. The sorption of gas mixtures in these materials suitable for CO₂ separation has been characterized experimentally in previous works, which showed that these systems exhibit rather marked deviations from the ideal pure-gas behavior, especially due to competitive effects. The accuracy of the DMS model in representing the non-idealities that arise during mixed-gas sorption was assessed in a wide range of temperatures, pressures and compositions, by comparing with the experimental results available. Using the parameters obtained from the best fit of pure-gas sorption isotherms, the agreement between the mixed-gas calculations and the experimental data varied greatly in the different cases inspected, especially in the case of CH₄ absorbed in mixed-gas conditions. A sensitivity analysis revealed that pure-gas data can be represented with the same accuracy by several different parameter sets, which, however, yield markedly different mixed-gas predictions, that, in some cases, agree with the experimental data only qualitatively. However, the multicomponent calculations with the DMS model yield more reliable results than the use of pure-gas data in the estimation of the solubility-selectivity of the material.

Translational and rotational dynamics of an ultra-thin nanorod probe particle in linear polymer melts

Translational and rotational dynamics of a single rigid ultra-thin nanorod probe particle in linear polymer melts are investigated using coarse-grained molecular dynamics (CG-MD) simulations.