Molecular Dynamics Simulations of Ion-Containing Polymers Using Generic Coarse-Grained Models

Microscopic theory of activated ion hopping in polymerized ionic liquids and glasses

We combine polymer integral equation theory of structure with microscopic dynamical theories of activated relaxation to formulate a theory of ion hopping in supercooled polymerized ionic liquids (PolyILs) and glasses. Activation barriers and the mean ion relaxation time are analyzed as a function of the ion-to-monomer size ratio, polymer persistence length, intrachain degree of dynamic cooperativity, anion-cation Coulomb attraction strength, and dielectric constant. A general finding is the dominance of Coulomb cage correlations and anion-cation attractions in determining the hopping rate of the small ions studied. A critical finding is that the activation barrier exists only above a threshold value of the system-specific dimensionless Coulomb attraction strength. As a consequence, the barrier grows in a highly nonlinear manner with anion-cation attraction energy. This suggests a route to super-ionic transport via a relatively modest reduction of the Coulombic association energy, an effect that becomes more dramatic the smaller the mobile ion. The temperature-dependent growth of the ion relaxation time is non-Arrhenius in the supercooled liquid, but may, or may not, crossover to an apparent Arrhenius form in the glass depending on how the dielectric constant on the relevant timescale changes with temperature. The magnitude of dynamic decoupling between the ion and polymer alpha relaxation times at the laboratory glass transition, the degree of trajectory level coupling of the ion and monomer motion, and ion jump lengths are also determined. A high level discussion of the connections between theory, experiments, and simulations, and a quantitative application to specific lithium, sodium, and potassium PolyILs, are presented.

Molecular Understanding of Polyaniline in Imidazolium-Based Ionic Liquid and Water Mixtures: A Molecular Dynamics Simulation Study

Monitoring interactive influence of cations and anions is inevitable for the development of conductive membranes using polyaniline (PANI). Herein, emeraldine base (EB) and emeraldine salt (ES) forms of PANI structural properties are understood in different imidazolium ionic liquid-water mixtures using molecular dynamics (MD) simulations. The conformational and structural properties of PANI using the combinations of two cations (1-ethyl-3-methylimidazolium [EMIM]+ and 1-butyl-3-methylimidazolium [BMIM]+) and five anions (acetate [ACT]-, formate [FRM]-, trifluoromethyl-sulfonate [TFS]-, benzoate [BEZ]-, and nitrate [NO3]-) are calculated. Based on various structural properties, it is understood that the anions play a dominant interaction with EB or ES compared to cations. Interestingly, it is observed that the radius of gyration shows an increase with [BMIM]+ and a decrease with [EMIM]+ with respect to the increasing size of the anion. There is a decrease in van der Waals interaction for ES due to the elongation of the chain when compared to EB. The excess molar volume shows more solvation behavior for ES than EB. Nevertheless, an increase in anion size leads to the favorable solvation of EB and ES. These observations help in the selection of the best combination of ILs for the sustainable designing of polymer membranes and their applications.

ART-SM: Boosting Fragment-Based Backmapping by Machine Learning

In sequential multiscale molecular dynamics simulations, which advantageously combine the increased sampling and dynamics at coarse-grained resolution with the higher accuracy of atomistic simulations, the resolution is altered over time. While coarse-graining is straightforward once the mapping between atomistic and coarse-grained resolution is defined, reintroducing the atomistic details is still a nontrivial process called backmapping. Here, we present ART-SM, a fragment-based backmapping framework that learns from atomistic simulation data to seamlessly switch from coarse-grained to atomistic resolution. ART-SM requires minimal user input and goes beyond state-of-the-art fragment-based approaches by selecting from multiple conformations per fragment via machine learning to simultaneously reflect the coarse-grained structure and the Boltzmann distribution. Additionally, we introduce a novel refinement step to connect individual fragments by optimizing specific bonds, angles, and dihedral angles in the backmapping process. We demonstrate that our algorithm accurately restores the atomistic bond length, angle, and dihedral angle distributions for various small and linear molecules from Martini coarse-grained beads and that the resulting high-resolution structures are representative of the input coarse-grained conformations. Moreover, the reconstruction of the TIP3P water model is fast and robust, and we demonstrate that ART-SM can be applied to larger linear molecules as well. To illustrate the efficiency of the local and autoregressive approach of ART-SM, we simulated a large realistic system containing the surfactants TAPB and SDS in solution using the Martini3 force field. The self-assembled micelles of various shapes were backmapped with ART-SM after training on only short atomistic simulations of a single water-solvated SDS or TAPB molecule. Together, these results indicate the potential for the method to be extended to more complex molecules such as lipids, proteins, macromolecules, and materials in the future.

Self-Assembled Oligomers Facilitate Amino Acid-Driven CO2 Capture at the Air-Aqueous Interface

Direct air capture of CO2 using amino acid absorbents, such as glycine or sarcosine, is constrained by the relatively slow mass transfer of CO2 through the air-aqueous interface. Our recent study showed a marked improvement in CO2 capture by introducing CO2-permeable oligo-dimethylsiloxane (ODMS-MIM+) oligomers with cationic (imidazolium, MIM+) headgroups. In this work, we have employed all-atom molecular dynamics simulations in combination with subensemble analysis using network theory to provide a detailed molecular picture of the behavior of CO2 and the glycinate anions (Gly-) at the ODMS-MIM+ decorated air-aqueous interfaces. We show that the cationic head groups of the surfactants enhance the concentration and lifetime of Gly- in the interfacial region, while ODMS tails promote the physisorption of CO2 in the interfacial region. Together, these two factors increase the effective region of contact and the probability of interactions between CO2 and Gly- compared to that of the pure air-aqueous interface. The fundamental insights gained in this work establish essential foundations for developing hybrid systems with oligomer-decorated interfaces to maximize the overall CO2 capture rates.

Controlling Electrostatics To Enhance Conductivity in Structured Electrolytes

Solid-state electrolytes are currently being explored as a safe material capable of addressing consumer energy-storage demands. Solid polymer electrolytes, in particular, offer a high energy density and improved safety when compared to liquid-based electrolytes, but tend to have a significantly lower ionic conductivity. We hypothesize structured ionic liquids can enhance conductivity compared to polymer electrolytes. Here, we explore the performance of these materials through coarse-grained molecular dynamics simulation. While we observe similar phase behavior (incorporating solid, smectic, and liquid phases) to that seen in experiments, we also observe significantly more mobility in the cationic species compared to the anionic species before the system reaches an arrest transition. We further discuss how the general results within this paper can guide further studies and target the design of new highly conductive solid electrolytes with the potential to enable the use of multivalent ionic species as ion conductors.

Morphology-Transport Coupling and Dissipative Structures in PEO-PS+LiTFSI Electrolytes In-Operando Conditions

A Single-Chain-in-Mean-Field (SCMF) algorithm was introduced to study block copolymer electrolytes in nonequilibrium conditions. This method self-consistently combines a particle-based description of the polymer with a generalized diffusion equation for the ionic fluxes, thus exploiting the time scale separation between fast ion motion and the slow polymer relaxation and self-assembly. We apply this computational method to study ion fluxes in electrochemical cells containing poly(ethylene oxide)-polystyrene (PEO-PS) block copolymers with added lithium salt. Blocking of the anion fluxes by the electrodes in-operando conditions polarizes the cells and results in an inhomogeneous salt-concentration profile. This gradient of salt concentration triggers lamellae-to-disorder and disorder-to-lamellae transitions near the electrodes, in good agreement with previous experimental observations. The effects of the selectivity of the electrode surface, the salt concentration and the voltage applied to the cell are systematically studied. For PEO-selective surfaces, the lamellae parallel to the electrode that forms at low applied potentials transition to a bicontinuous morphology at high applied potentials in order to allow ion transport through the insulating PS layers. The formation of this dissipative structure, which is unexpected considering the equilibrium behavior of the material, is in line with the principle of maximum entropy production. In summary, the transport and morphology in PEO-PS electrolytes are strongly coupled: ionic currents influence self-assembly, which in turn modulates the ionic fluxes in the cell.

Accelerated Prediction of Phase Behavior for Block Copolymer Libraries Using a Molecularly Informed Field Theory

Solution formulations involving polymers are the basis for a wide range of products spanning consumer care, therapeutics, lubricants, adhesives, and coatings. These multicomponent systems typically show rich self-assembly and phase behavior that are sensitive to even small changes in chemistry and composition. Longstanding computational efforts have sought techniques for predictive modeling of formulation structure and thermodynamics without experimental guidance, but the challenges of addressing the long time scales and large length scales of self-assembly while maintaining chemical specificity have thwarted the emergence of general approaches. As a consequence, current formulation design remains largely Edisonian. Here, we present a multiscale modeling approach that accurately predicts, without any experimental input, the complete temperature-concentration phase diagram of model diblock polymers in solution, as established postprediction through small-angle X-ray scattering. The methodology employs a strategy whereby atomistic molecular dynamics simulations is used to parametrize coarse-grained field-theoretic models; simulations of the latter then easily surmount long equilibration time scales and enable rigorous determination of solution structures and phase behavior. This systematic and predictive approach, accelerated by access to well-defined block copolymers, has the potential to expedite in silico screening of novel formulations to significantly reduce trial-and-error experimental design and to guide selection of components and compositions across a vast range of applications.

Dynamic density functional theory of polymers with salt in electric fields

We present a dynamic density functional theory for modeling the effects of applied electric fields on the local structure of polymers with added salt (polymer electrolytes). Time-dependent equations for the local electrostatic potential and volume fractions of polymer, cation, and anion of added salt are developed using the principles of linear irreversible thermodynamics. For such a development, a field theoretic description of the free energy of polymer melts doped with salts is used, which captures the effects of local variations in the dielectric function. Connections of the dynamic density functional theory with experiments are established by relating the three phenomenological Onsager's transport coefficients of the theory to the mutual diffusion of electrolyte, ionic conductivity, and transference number of one of the ions. The theory is connected with a statistical mechanical model developed by Bearman and Kirkwood [J. Chem. Phys. 28, 136 (1958)] after relating the three transport coefficients to friction coefficients. The steady-state limit of the dynamic density functional theory is used to understand the effects of dielectric inhomogeneity on the phase separation in polymer electrolytes. The theory developed here provides not only a way to connect with experiments but also to develop multi-scale models for studying connections between local structure and ion transport in polymer electrolytes.

Understanding Ion Distribution and Diffusion in Solid Polymer Electrolytes

Solid polymer electrolytes (SPEs)─polymer melts with added salts─exhibit ion conduction and high mechanical properties, and are thus promising materials for future energy storage devices. The ion conductivity in an SPE is intricately connected to the salt ion distribution in the polymer matrix. The relationship between ion diffusion and ion distribution in SPEs remains unresolved. Here, we conduct coarse-grained molecular dynamics simulations and establish correlations between ion distribution and transport for a phenomenological SPE model. We propose phase diagrams of SPEs as a function of ion pair size, ion concentration, and the Bjerrum length of the material. A crossover from a discrete ion aggregate to a percolated ion aggregate is demonstrated as a function of ion pair size for low ion concentration in the SPE. The ion diffusion shows a strong correlation with its size, as has been found experimentally. The work provides important design strategies for controlling the ion distribution and enhancing ion conductivity in a polymer matrix.

Dilute polyelectrolyte solutions: recent progress and open questions

Polyelectrolytes are a class of polymers possessing ionic groups on their repeating units. Since counterions can dissociate from the polymer backbone, polyelectrolyte chains are strongly influenced by electrostatic interactions. As a result, the physical properties of polyelectrolyte solutions are significantly different from those of electrically neutral polymers. The aim of this article is to highlight key results and some outstanding questions in the polyelectrolyte research from recent literature. We focus on the influence of electrostatics on conformational and hydrodynamic properties of polyelectrolyte chains. A compilation of experimental results from the literature reveals significant disparities with theoretical predictions. We also discuss a new class of polyelectrolytes called poly(ionic liquid)s that exhibit unique physical properties in comparison to ordinary polyelectrolytes. We conclude this review by listing some key research challenges in order to fully understand the conformation and dynamics of polyelectrolytes in solutions.

Ion Conductivity in Salt-Doped Polymers: Combined Effects of Temperature and Salt Concentration

We construct a coarse-grained molecular dynamics model based on poly(ethylene oxide) and lithium bis(trifluoromethane)sulfonimide salt to examine the combined effects of temperature and salt concentration on the transport properties. Salt doping notably slows the dynamics of polymer chains and reduces ion diffusivity, resulting in a glass transition temperature increase proportional to the salt concentration. The polymer diffusion is shown to be well represented by a modified Vogel-Fulcher-Tamman (M-VFT) equation that accounts for both the temperature and salt concentration dependence. Furthermore, we find that, at any temperature, the concentration dependence of the conductivity is well described by the product of its infinite dilution value and a correction factor accounting for the reduced segmental mobility with increasing salt concentration. These results highlight the important role of polymer segmental mobility in the salt concentration dependence of ion conductivity for temperatures near and above the glass transition.

Control of polymer-protein interactions by tuning the composition and length of polymer chains

Nanomoduling the 3D shape and chemical functionalities in a synthetic polymer may create recognition cavities for biomacromolecule binding, which serves as an attractive alternative to natural antibodies with much less cost. To obtain fundamental understanding and predict molecular design rules of the polymer antibody, we analyze the complex structure between the biomarker protein epithelial cell adhesion molecule (EpCAM) and a series of polymer ligands via molecular dynamics (MD) simulations. For monomeric ligands, strong enrichment of aromatic residues in protein binding sites is revealed, in line with the reported observations for natural antibodies. Yet, for linear polymers with a growing degree of polymerization, for the first time, a drastic change is revealed on the type of enriched protein residues and the location of protein binding sites, driven by the increasing steric hindrance effect that makes the adsorption of the polymer in the protein exterior feasible. Varying the polymer length and monomeric composition also significantly affects the ligand binding affinity. Here, we have captured three distinct dependences of the ligand binding free energy on the degree of polymerization: for NIPAm based hydrophilic polymers, TBAm dominated hydrophobic polymers and AAc dominated charged polymers. These results can be rationalized by the complex structure and the composition of protein residues at the binding interface. The entire analysis demonstrates unique binding features for polymer ligands and the possibility to modulate their binding sites and affinity by engineering the polymer structure.

Antiemetic effects of sclareol, possibly through 5-HT3 and D2 receptor interaction pathways: In-vivo and in-silico studies

BACKGROUND

Emesis is a complex physiological phenomenon that serves as a defense against numerous toxins, stressful situations, adverse medication responses, chemotherapy, and movement. Nevertheless, preventing emesis during chemotherapy or other situations is a significant issue for researchers. Hence, the majority view contends that successfully combining therapy is the best course of action. In-vivo analysis offers a more comprehensive grasp of how compounds behave within a complex biological environment, whereas in-silico evaluation refers to the use of computational models to forecast biological interactions.

OBJECTIVES

The objectives of the present study were to evaluate the effects of Sclareol (SCL) on copper sulphate-induced emetic chicks and to investigate the combined effects of these compounds using a conventional co-treatment approach and in-silico study.

METHODS

SCL (5, 10, and 15 mg/kg) administered orally with or without pre-treatment with anti-emetic drugs (Ondansetron (ODN): 24 mg/kg, Domperidone (DOM): 80 mg/kg, Hyoscine butylbromide (HYS): 100 mg/kg, and Promethazine hydrochloride (PRO): 100 mg/kg) to illustrate the effects and the potential involvement with 5HT3, D2, M3/AChM, H1, or NK1 receptors by SCL. Furthermore, an in-silico analysis was conducted to forecast the role of these receptors in the emetic process.

RESULTS

The results suggest that SCL exerted a dose-dependent anti-emetic effect on the chicks. Pretreatment with SCL-10 significantly minimized the number of retches and lengthened the emesis tendency of the experimental animals. SCL-10 significantly increased the anti-emetic effects of ODN and DOM. However, compared to the ODN-treated group, (SCL-10 + ODN) group considerably (p < 0.0001) extended the latency duration (109.40 ± 1.03 s) and significantly (p < 0.01) decreased the number of retches (20.00 ± 0.70), indicating an anti-emetic effect on the test animals. In in-silico analysis, SCL exhibited promising binding affinities with suggesting receptors.

CONCLUSION

SCL-10 exerted an inhibitory-like effect on emetic chicks, probably through the interaction of the 5HT3 and D2 receptors. Further studies are highly appreciated to validate this study and determine the precise mechanism(s) behind the anti-emetic effects of SCL. We expect that SCL-10 may be utilized as an antiemetic treatment in a single dosage form or that it may function as a synergist with other traditional medicines.

Molecular Origin of High Cation Transference in Mixtures of Poly(pentyl malonate) and Lithium Salt

The rational development of new electrolytes for lithium batteries rests on the molecular-level understanding of ion transport. We use molecular dynamics simulations to study the differences between a recently developed promising polymer electrolyte based on poly(pentyl malonate) (PPM) and the well-established poly(ethylene oxide) (PEO) electrolyte; LiTFSI is the salt used in both electrolytes. Cation transference is calculated by tracking the correlated motion of different species. The PEO solvation cage primarily contains 1 chain, resulting in strong correlations between Li⁺ and the polymer. In contrast, the PPM solvation cage contains multiple chains, resulting in weak correlations between Li⁺ and the polymer. This difference results in a high cation transference in PPM relative to PEO. Our comparative study suggests possible designs of polymer electrolytes with ion transport properties better than both PPM and PEO. The solvation cage of such a hypothetical polymer electrolyte is proposed based on insights from our simulations.

Specific ion effects on the aggregation of polysaccharide-based polyelectrolyte complex particles induced by monovalent ions within Hofmeister series

Polysaccharide-based polyelectrolyte complex (PEC) particles have been utilized as carriers for drug delivery systems (DDS) and as building components for material development. Despite their versatility, the aggregation mechanism of PEC particles in the presence of salts remains unclear. To clarify the aggregation mechanism, the specific ion effects of monovalent salts within the Hofmeister series on the aggregation behavior of PEC particles composed of chitosan and chondroitin sulfate C, which are often used as DDS carriers and materials, were studied. Here, we found that weakly hydrated chaotropic anions promoted the aggregation of positively charged PEC particles. The hydrophobicity of the PEC particles was increased by these ions. Strongly hydrated ions such as Cl^- are less likely to accumulate in these particles, whereas weakly hydrated chaotropic ions such as SCN^- are more likely to accumulate. Molecular dynamics simulations suggested that the hydrophobicity of PECs might be strengthened by ions due to changes in intrinsic and extrinsic ion pairs and hydrophobic interactions. Based on our results, it is expected that the control of surface hydrophilicity or hydrophobicity is an effective approach for controlling the stability of PEC particles in the presence of ions.

Assembly of polyelectrolyte star block copolymers at the oil-water interface

To understand and resolve adsorption, reconfiguration, and equilibrium conformations of charged star copolymers, we carried out an integrated experimental and coarse-grained molecular dynamics simulation study of the assembly process at the oil-water interface. This is important to guide development of novel surfactants or amphiphiles for chemical transformations and separations. The star block copolymer consisted of arms that are comprised of hydrophilic-hydrophobic block copolymers that are covalently tethered via the hydrophobic blocks to one point. The hydrophobic core represents polystyrene (PS) chains, while the hydrophilic corona represents quaternized poly(2-vinylpyridine) (P2VP) chains. The P2VP is modeled to become protonated when in contact with an acidic aqueous phase, thereby massively increasing the hydrophilicity of this block, and changing the nature of the star at the oil-water interface. This results in a configurational change whereby the chains comprising the hydrophilic corona are significantly stretched into the aqueous phase, while the hydrophobic core remains solubilized in the oil phase. In the simulations, we followed the kinetics of the anchoring and assembly of the star block copolymer at the interface, monitoring the lateral assembly, and the subsequent reconfiguration of the star via changes in the interfacial tension that varies as the degree-of-protonation increases. At low fractions of protonation, the arm cannot fully partition into the aqueous side of the interface and instead interacts with other arms in the oil phase forming a network near the interface. These insights were used to interpret the non-monotonic dependence of pH with the asymptotic interfacial tension from pendant drop tensiometry experiments and spectral signatures of aromatic stretches seen in vibrational sum frequency generation (SFG) spectroscopy. We describe the relationship of interfacial tension to the star assembly via the Frumkin isotherm, which phenomenologically describes anti-cooperativity in adsorbing stars to the interface due to crowding. Although our model explicitly considers long-range electrostatics, the contribution of electrostatics to interfacial tension is small and brought about by strong counterion condensation at the interface. These results provide key insights into resolving the adsorption, reconfiguration, and equilibrium conformations of charged star block copolymers as surfactants.

Predictive Molecular Models for Charged Materials Systems: From Energy Materials to Biomacromolecules

Electrostatic interactions play a dominant role in charged materials systems. Understanding the complex correlation between macroscopic properties with microscopic structures is of critical importance to develop rational design strategies for advanced materials. But the complexity of this challenging task is augmented by interfaces present in the charged materials systems, such as electrode-electrolyte interfaces or biological membranes. Over the last decades, predictive molecular simulations that are founded in fundamental physics and optimized for charged interfacial systems have proven their value in providing molecular understanding of physicochemical properties and functional mechanisms for diverse materials. Novel design strategies utilizing predictive models have been suggested as promising route for the rational design of materials with tailored properties. Here, an overview of recent advances in the understanding of charged interfacial systems aided by predictive molecular simulations is presented. Focusing on three types of charged interfaces found in energy materials and biomacromolecules, how the molecular models characterize ion structure, charge transport, morphology relation to the environment, and the thermodynamics/kinetics of molecular binding at the interfaces is discussed. The critical analysis brings two prominent field of energy materials and biological science under common perspective, to stimulate crossover in both research field that have been largely separated.

Perspective: Morphology and ion transport in ion-containing polymers from multiscale modeling and simulations

Ion-containing polymers are soft materials composed of polymeric chains and mobile ions. Over the past several decades they have been the focus of considerable research and development for their use as the electrolyte in energy conversion and storage devices. Recent and significant results obtained from multiscale simulations and modeling for proton exchange membranes (PEMs), anion exchange membranes (AEMs), and polymerized ionic liquids (polyILs) are reviewed. The interplay of morphology and ion transport is emphasized. We discuss the influences of polymer architecture, tethered ionic groups, rigidity of the backbone, solvents, and additives on both morphology and ion transport in terms of specific interactions. Novel design strategies are highlighted including precisely controlling molecular conformations to design highly ordered morphologies; tuning the solvation structure of hydronium or hydroxide ions in hydrated ion exchange membranes; turning negative ion-ion correlations to positive correlations to improve ionic conductivity in polyILs; and balancing the strength of noncovalent interactions. The design of single-ion conductors, well-defined supramolecular architectures with enhanced one-dimensional ion transport, and the understanding of the hierarchy of the specific interactions continue as challenges but promising goals for future research.

Macroscopic charge segregation in driven polyelectrolyte solutions

Understanding the behavior of charged complex fluids is crucial for a plethora of important industrial, technological, and medical applications. Here, using coarse-grained molecular dynamics simulations, we investigate the properties of a polyelectrolyte solution with explicit counterions and implicit solvent that is driven by a steady electric field. By properly tuning the interplay between interparticle electrostatics and the applied electric field, we uncover two non-equilibrium continuous phase transitions as a function of the driving field. The first transition occurs from a homogeneous mixed phase to a macroscopic charge-segregated phase in which the polyelectrolyte solution self-organizes to form two lanes of like-charges, parallel to the applied field. We show that the fundamental underlying factor responsible for the emergence of this charge segregation in the presence of an electric field is the excluded volume interactions of the drifting polyelectrolyte chains. As the driving field is increased further, a re-entrant transition is observed from a charge-segregated phase to a homogeneous phase. The re-entrance is signaled by a decrease in the mobility of the monomers and counterions as the electric field is increased. Furthermore, with multivalent counterions, a counterintuitive regime of negative differential mobility is observed in which the charges move progressively more slowly as the driving field is increased. We show that all these features can be consistently explained using an intuitive trapping mechanism that operates between the oppositely moving charges, and present numerical evidence to support our claims. Parameter dependencies and phase diagrams are studied to better understand charge segregation in such driven polyelectrolyte solutions.

Structure and Diffusion of Ionic PDMS Melts

Ionic polymers exhibit mechanical properties that can be widely tuned upon selectively charging them. However, the correlated structural and dynamical properties underlying the microscopic mechanism remain largely unexplored. Here, we investigate, for the first time, the structure and diffusion of randomly and end-functionalized ionic poly(dimethylsiloxane) (PDMS) melts with negatively charged bromide counterions, by means of atomistic molecular dynamics using a united atom model. In particular, we find that the density of the ionic PDMS melts exceeds the one of their neutral counterpart and increases as the charge density increases. The counterions are condensed to the cationic part of end-functionalized cationic PDMS chains, especially for the higher molecular weights, leading to a slow diffusion inside the melt; the counterions are also correlated more strongly to each other for the end-functionalized PDMS. Temperature has a weak effect on the counterion structure and leads to an Arrhenius type of behavior for the counterion diffusion coefficient. In addition, the charge density of PDMS chains enhances the diffusion of counterions especially at higher temperatures, but hinders PDMS chain dynamics. Neutral PDMS chains are shown to exhibit faster dynamics (diffusion) than ionic PDMS chains. These findings contribute to the theoretical description of the correlations between structure and dynamical properties of ion-containing polymers.

Toward Bottom-Up Understanding of Transport in Concentrated Battery Electrolytes

Bottom-up understanding of transport describes how molecular changes alter species concentrations and electrolyte voltage drops in operating batteries. Such an understanding is essential to predictively design electrolytes for desired transport behavior. We herein advocate building a structure-property-performance relationship as a systematic approach to accurate bottom-up understanding. To ensure generalization across salt concentrations as well as different electrolyte types and cell configurations, the property-performance relation must be described using Newman's concentrated solution theory. It uses Stefan-Maxwell diffusivity, ij , to describe the role of molecular motions at the continuum scale. The key challenge is to connect ij to the structure. We discuss existing methods for making such a connection, their peculiarities, and future directions to advance our understanding of electrolyte transport.

Coarse-Grained Modeling of Ion-Containing Polymers

Ion-containing polymers have continued to be an important research focus for several decades due to their use as an electrolyte in energy storage and conversion devices. Elucidation of connections between the mesoscopic structure and multiscale dynamics of the ions and solvent remains incompletely understood. Coarse-grained modeling provides an efficient approach for exploring the structural and dynamical properties of these soft materials. The unique physicochemical properties of such polymers are of broad interest. In this review, we summarize the current development and understanding of the structure-property relationship of ion-containing polymers and provide insights into the design of such materials determined from coarse-grained modeling and simulations accompanying significant advances in experimental strategies. We specifically concentrate on three types of ion-containing polymers: proton exchange membranes (PEMs), anion exchange membranes (AEMs), and polymerized ionic liquids (polyILs). We posit that insight into the similarities and differences in these materials will lead to guidance in the rational design of high-performance novel materials with improved properties for various power source technologies.

Structures of cationic and anionic polyelectrolytes in aqueous solutions: the sign effect

In this study, we use molecular dynamics simulation to explore the structures of anionic and cationic polyelectrolytes in aqueous solutions. We first confirm the significantly stronger solvation effects of single anions compared to cations in water at the fixed ion radii, due to the reversal orientations of asymmetric dipolar H₂O molecules around the ions. Based on this, we demonstrate that the solvation discrepancy of cations/anions and electrostatic correlations of ionic species can synergistically cause the nontrivial structural difference between single anionic and cationic polyelectrolytes. The cationic polyelectrolyte shows an extended structure whereas the anionic polyelectrolyte exhibits a collapsed structure, and their structural differences decline with increasing the counterion size. Furthermore, we corroborate that multiple cationic polyelectrolytes or multiple anionic polyelectrolytes can exhibit largely differential molecular architectures in aqueous solutions. In the solvation dominant regime, the polyelectrolyte solutions exhibit uniform structures; whereas, in the electrostatic correlation dominant regime, the polyelectrolyte solutions exhibit heterogeneous structures, in which the likely charged chains microscopically aggregate through counterion condensations. Increasing the intrinsic chain rigidity causes polyelectrolyte extension and hence moderately weakens the inter-chain clustering. Our work highlights the various, unique structures and molecular architectures of polyelectrolytes in solutions caused by the multi-body correlations between polyelectrolytes, counterions and asymmetric dipolar solvent molecules, which provides insights into the fundamental understanding of ion-containing polymers.

Data-Driven Methods for Accelerating Polymer Design

Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.

Operando Characterization of Organic Mixed Ionic/Electronic Conducting Materials

Operando characterization plays an important role in revealing the structure-property relationships of organic mixed ionic/electronic conductors (OMIECs), enabling the direct observation of dynamic changes during device operation and thus guiding the development of new materials. This review focuses on the application of different operando characterization techniques in the study of OMIECs, highlighting the time-dependent and bias-dependent structure, composition, and morphology information extracted from these techniques. We first illustrate the needs, requirements, and challenges of operando characterization then provide an overview of relevant experimental techniques, including spectroscopy, scattering, microbalance, microprobe, and electron microscopy. We also compare different in silico methods and discuss the interplay of these computational methods with experimental techniques. Finally, we provide an outlook on the future development of operando for OMIEC-based devices and look toward multimodal operando techniques for more comprehensive and accurate description of OMIECs.

Glass transition of ion-containing polymer melts in bulk and thin films

Ion-containing polymers often are good glass formers, and the glass transition temperature is an important parameter to consider for practical applications, which prescribes the working temperature range for different mechanical and dynamic properties. In this work, we present a systematic molecular dynamics simulation study on the coupling of ionic correlations with the glass transition, based on a generic coarse-grained model of ionic polymers. The variation of the glass transition temperature is examined concerning the influence of the electrostatic interaction strength, charge fraction, and charge sequence. The interplay with the film thickness effect is also discussed. Our results reveal a few typical features about the glass transition process that are in qualitative agreement with previous studies, further highlighting the effects of counterion entropy at weak ionic correlations and physical crosslinking of ionic aggregates at strong ionic correlations. Detailed parametric dependencies are displayed, which demonstrate that introducing strong ionic correlations promotes vitrification while adopting a precise charge sequence and applying strong confinement with weak surface affinity reduce the glass transition temperature. Overall, our investigation provides an improved picture towards a comprehensive understanding of the glass transition in ion-containing polymeric systems from a molecular simulation perspective.

Polyelectrolyte Complex Coacervation across a Broad Range of Charge Densities

Polyelectrolyte complex coacervates of homologous (co)polyelectrolytes with a near-ideally random distribution of a charged and neutral ethylene oxide comonomer were synthesized. The unique platform provided by these building blocks enabled an investigation of the phase behavior across charge fractions 0.10 ≤ f ≤ 1.0. Experimental phase diagrams for f = 0.30-1.0 were obtained from thermogravimetric analysis of complex and supernatant phases and contrasted with molecular dynamics simulations and theoretical scaling laws. At intermediate to high f, a dependence of polymer weight fraction in the salt-free coacervate phase (w P,c) of w P,c ∼ f 0.37±0.01 was extracted; this trend was in good agreement with accompanying simulation predictions. Below f = 0.50, w P,c was found to decrease more dramatically, qualitatively in line with theory and simulations predicting an exponent of 2/3 at f ≤ 0.25. Preferential salt partitioning to either coacervate or supernatant was found to be dictated by the chemistry of the constituent (co)polyelectrolytes.

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.