Superionic conduction in solid polymer electrolytes – decoupling ion transport from segmental relaxation

Abstract

Solvent-free, solid polymer electrolytes (SPEs) are promising candidates for next-generation, electrochemical energy storage systems due to their potential to enhance safety and performance, enable flexible device architectures, and streamline manufacturing processes. Conventional SPEs suffer from limited ionic conductivity due to the strong coupling between ion transport and (generally slow) polymer segmental relaxation. The realization of superionic conduction in SPEs, in which ions move faster than the structural relaxation of the polymers, requires a shift in design principles to promote this type of decoupled ion motion. In this perspective, we discuss how polymer architecture, ion–ion correlations, and ion–polymer interactions can unlock superionic behavior. We highlight several key design features, such as crystallinity, bulky side groups, high molecular weight, and percolating ionic aggregation, with a focus on creating low-barrier transport pathways in various polymer systems. We also demonstrate opportunities to combine polymer chemistry and data science through high-throughput and automated screening approaches to reveal how phase behavior, ion dynamics, and ionic interactions govern transport, thereby potentially enabling data-driven discovery of superionic polymer electrolyte materials.

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Introduction

Lithium–ion batteries (LiBs) are a key technology for powering portable electronics, electric vehicles, and grid-scale storage systems due to their high energy densities, low self-discharge rates, and minimal memory effects.^1–7 LiBs consist of one or more connected electrochemical cells,⁸ and each cell contains a cathode and an anode separated by an electrolyte-separator system. Conventional electrolytes typically comprise a lithium salt (e.g., LiPF₆) and stabilizing additives dissolved in a mixture of carbonate-based liquid solvents (e.g., ethylene carbonate and dimethyl carbonate).^2,9 Although this class of electrolytes offers high ionic conductivities at room temperature (∼10⁻² S cm⁻¹),^2,10 it suffers from low lithium transference numbers (i.e., fraction of current carried by Li⁺, t_Li⁺, <0.5). The low t_Li⁺s result from a transport mismatch between Li⁺ and the anion. Lithium ions are surrounded by bulky solvation shells, which significantly slow their movement in comparison to the anions.¹¹ This imbalance can lead to concentration gradients during battery operation, eventually resulting in salt depletion at one electrode and precipitation at the other.¹² Over time, this effect can cause battery failure, and in severe cases, even catastrophic explosions.¹³ Additionally, the low t_Li⁺s limit the current density that the battery can support, thereby restricting charge and discharge rates.¹⁴

To meet the demand for higher-performing LiBs, new electrolyte systems must be developed to address the above-mentioned safety and performance challenges. Solid polymer electrolytes (SPEs) are one attractive option due to their versatility and processability.¹⁵ Conventional SPEs, typically flexible polymers with glass transition temperatures (T_gs) below 25 °C, rely on segmental mobility to create a dynamic environment for ion transport.^16,17 The chain flexibility of these SPEs promotes the formation of closely packed structures with limited free volume between polymer chains.¹⁸ As a result, ions move only when the polymer matrix undergoes local movement, i.e., polymer segmental motion,^19,20 which is inherently slow and becomes even slower with increasing ion concentration, as more ions are coordinated to the polymer – ultimately resulting in reduced conductivities.²¹

To overcome this limitation, one can consider superionic transport, in which ions move faster than the polymer matrix relaxes by decoupling of ion motion from polymer segmental dynamics. Decoupled ion transport typically can be identified through a modified Walden plot analysis, in which molar conductivity is plotted against the inverse of segmental relaxation time,¹⁸ as shown in Fig. 1. τ is determined from the frequency at the crossover of the storage and loss moduli at high frequencies.¹⁸ A solid line with a slope of 1 represents the ideal Walden line, which was established using reference systems of dilute aqueous solutions of salts (e.g., KCl, LiCl, LiClO₄),²² wherein the ionic interaction is much weaker than thermal energy.²² The ideal Walden line divides the Walden plot into a superionic region (above the line) and a subionic region (below the line).²² Systems that fall on the Walden line indicate complete dissociation of ion pairs and that ion motion is closely coupled with the segmental motion,^18,22 whereas systems in the subionic region typically have low free ion concentrations.²² Superionic electrolytes have ions moving faster than the structural relaxation of the polymers.²² The degree of decoupling, ε, can be calculated as 1 − α, in which α is the modified Walden plot slope. This parameter quantifies the extent to which ionic conductivity becomes decoupled from segmental relaxation in the vicinity of T_g.²³

Fig. 1 A typical Walden plot modified for SPEs. Adapted from ref. 23. Copyright 2013 Elsevier B.V.

Another approach to characterizing ionic motion that is independent of segmental relaxation is to examine Arrhenius-type temperature-dependent behavior,^24–27 that is, the conductivity follows the Arrhenius equation [eqn (1)],²⁸

in which σ₀ is the ionic conductivity at infinitely high temperature,^4,29 E_a is the pseudo activation energy associated with ion hopping, R is the ideal gas constant, and T is the absolute temperature in Kelvin.

To facilitate superionic transport, percolating free volume can be leveraged to promote ion hopping through interconnected voids.^30,31 One definition of free volume is the total volume of a system minus the volume occupied by the molecules.³² Free-volume voids can originate from imperfect chain packing (e.g., packing frustration).^33–35 The total free volume and the size distribution of these free-volume voids can be characterized using several experimental techniques such as, positron annihilation lifetime spectroscopy, which probes positron lifetimes in the material,^36–39 inverse gas chromatography, in which the average void size is inferred from the size of the sorbate molecule,³⁷ and ¹²⁹Xe NMR spectroscopy, in which the ¹²⁹Xe chemical shift relative to a gas-phase reference correlates with the free-volume void size.^37,40–42

Generally, three key factors can impart chain packing and associated free volume: chain connectivity, chain semi-flexibility, and monomer structure.⁴³ The connectivity between repeat units restricts their mobility; therefore, longer chains increase the degree of packing frustration.⁴⁴ Additionally, bulky backbones and/or side groups limit chain rearrangement, leading to packing-frustrated polymers and decoupled ion transport,^43,45 as demonstrated in loosely packed poly(ionic liquids)^44,46–48 and single-ion-conducting (SIC) polymer blends.²⁰ Apart from free volume-support transport, ion hopping through percolating aggregates is another way to obtain superionic transport. For example, in SIC polymers, both molecular dynamics (MD) simulations and experimental results suggested that superionic conduction is achievable via hopping through chain-like aggregates, in which one-dimensional ionic channels (due to the hexagonal morphology) and immobilized anion were key determinants.^49,50 In general, it is desirable if the SPE contains a continuous network of free volume voids for Li⁺ transport while restricting the motion of bulkier anions to enhance favorable Li⁺ conduction.^19,51 To this end, understanding the impacts of various parameters (polymer structure and architecture, ion-ion correlation, and ion–polymer interaction; see Fig. 2) on superionic transport can provide important insights into efficient SPE design.

Fig. 2 Key parameters influencing superionic conduction.

Polymer structure and architecture

To achieve fast and selective ion transport that is decoupled from polymer segmental dynamics, a pathway is needed through which ions can move without relying on host-polymer relaxation.¹⁶ de Pablo and coworkers demonstrated that in graft polyethers [poly(oligo-oxyethylene methyl ether methacrylate) (POEM_x), in which x represented the number of ethylene oxide (EO) units in the grafted side chain], two different ion transport events with varying characteristic times were present – a fast intrachain hopping alongside chains and a slow interchain hopping between side chains.⁵² Beyond PEO-based materials, similar architecture-dependent transport behavior has been noted in other polymer systems. For example, Chen et al. simulated a comb-branched sodium poly[(4-styrenesulfonyl) (trifluoromethanesulfonyl) imide] P(STFSINa) homopolymer and its copolymers with either ether or styrene spacer groups (see Fig. 3) and showed that varying the spacer polarity leads to different ion transport mechanisms.⁵³

Fig. 3 Chemical structures of the (a) P[STFSINa] homopolymer and copolymers with a (b) polystyrene and (c) polyether spacer. Reprinted from ref. 53. Copyright (2016), with permission from Elsevier.

The ion hopping mechanism was more dominant in the PSTFSINa homopolymer than in the copolymer systems, due to disruptions of the percolating aggregate network caused by the introduction of polar or non-polar spacers.⁵³ This hopping motion was also noted in Li⁺-based SIC SPEs, in which Kadulkar et al. leveraged both experiments and simulations to probe a series of comb-branched SIC copolymers consisting of poly(ethylene glycol) methyl ether acrylate (PEGMEA) with different lithiated anionic groups – acrylic acetate (AA), 2-acrylamido-2-methylpropanesulfonate (AMPS), and methacrylate-propyl (trifluoromethanesulfonyl) imide (MPTFSI).⁵⁴ Copolymers with increasingly bulky anions exhibited a transition in the transport mechanism – from segmental-dynamics-supported motion to hopping motion.⁵⁴ MD simulations revealed that Li⁺ dissociated more rapidly from the larger MPTFSI vs. AMPS and AA.⁵⁴ This dissociation likely led to faster local ion exchange times within the ionic aggregates, maximizing decoupled Li⁺ transport.⁵⁴ Although t_Li⁺ results were not reported, it was suggested that all the systems should have t_Li⁺s of ∼0.98, determined from the ratio of the molar masses of the anionic repeat unit to Li⁺.^54–56

Decoupled transport also can be promoted within the crystalline state of a polymer. Bruce and coworkers demonstrated that in crystalline PEO doped with Li salts (LiPF₆, LiAsF₆, LiSbF₆, or LiClO₄), a co-crystalline phase generated well-defined, cylindrical channels, within which Li⁺ was coordinated by the ether oxygens and underwent hopping-related motion.^17,57 The anions were located outside of these channels and did not actively participate in Li⁺ coordination (Fig. 4).^17,57 It has been suggested that ion transport in crystalline regions of PEO is closely related to the molecular alignment of the chains and the presence of the Li⁺ defects within polymer tunnels.^24,58 Both features collectively promote Li⁺ hopping between sites along the tunnels. The temperature-dependent ionic conductivities followed an Arrhenius relationship.^24–26 Moreover, the ionic conductivities in semi-crystalline PEO/salt systems were almost two orders of magnitude higher than those of amorphous PEO-like SPEs at similar compositions and molecular weights.^17,57 Staunton et al. found that in semi-crystalline LiPF₆-doped PEO systems, the chain-end chemistry significantly impacted ionic conductivities.²⁹ By replacing the –OCH₃ end group with a bulkier –OC₂H₅ group, a 10× increase in conductivities was realized, despite both systems having similar crystal structures and activation energies²⁹ (estimated from the fitting parameter in eqn (1)).²⁸ The preservation of the crystal structures in the presence of a bulkier –OC₂H₅ end group requires a greater extent of local disorder relative to a –OCH₃ end group.²⁹ This disorder likely increased the number of mobile charge carriers, enhancing the pre-exponential factor in conductivity without altering the energy barrier for ion transport.²⁹

Fig. 4 Schematic of Li⁺ diffusion pathways in PEO₆/LiPF₆, in which PEO₆ indicates that one Li⁺ is coordinated by six ether oxygens. Thin lines represent coordination around Li⁺. Solid blue spheres indicate lithium in the five-coordinate crystallographic site; meshed blue spheres represent lithium in the intermediate four-coordinate site. Representations of carbon atoms are shown in green, and oxygen atoms in red. Adapted with permission from ref. 57. Copyright 2003 American Chemical Society.

A key challenge in enabling decoupled transport lies in constructing continuous, low-barrier pathways that support efficient ion hopping across different polymer systems. The intrinsic free-volume voids vary in size and are highly dynamic, which can impact transport as they change shape, size distribution, and connectivity. Moreover, ion hopping within ionic aggregates is facilitated when the aggregates form a percolating network, whereas discrete ionic clusters increase transport barriers.

Polymer architecture not only closely correlates with the mode of ion conduction but also influences polymer fragility. Sokolov and co-workers highlighted that the decoupling depends on the system's fragility index (m), i.e., the rate of change of segmental relaxation time (τ) as a function of temperature near T_g [eqn (2)],⁴⁶

Their work suggested that fragile polymer systems, i.e., those having high energy barriers for intrachain rotations and conformational changes, exhibited significant decoupling of ion transport from polymer segmental relaxation.⁴⁶ In these glassy systems, limited chain flexibility prevents close packing, leaving unstructured free volume that forms percolating networks and facilitates ion hopping.^16,59 The team studied a series of poly(1-butyl-3-vinylimidazolium)-based poly(ionic liquids) (PILs) with varying pendent groups (Fig. 5a).¹⁸ Decoupled transport was assessed through two routes: (1) PILs exhibited conductivities several orders of magnitude higher than 10⁻¹⁵ S cm⁻¹ (Fig. 5b) at their T_gs, whereas systems in which ion motion is fully coupled to structural relaxation are expected to have conductivities on the order of 10⁻¹⁵ S cm⁻¹ at T_g;⁶⁰ and (2) the degree of decoupling, ε, was calculated as 1 − α, in which α is the Walden plot slope, and ε increased from 0.34 to 0.60 with increasing m of the PILs.¹⁸

Fig. 5 (a) Chemical structures of PILs with different pendent groups. (b) Ionic conductivity vs. T_g/T. (c) Walden plot (modified for SPEs). The decoupling exponent, ε = 1 − α, reflects the degree of decoupling between the ionic conductivity and segmental relaxation in the vicinity of T_g. Adapted with permission from ref. 18. Copyright 2015 American Chemical Society.

The flexibility or rigidity of the polymer backbone and pendent groups also influences both polymer fragility and the extent of decoupling. Wojnarowska et al. investigated three aprotic PILs that shared the same ion-counterion pair but differed in side groups and backbone structure.⁴⁸ The side groups included a rigid ethylimidazolium cation, a rigid imidazolium cation connected to an alkyl chain, or a semiflexible imidazolium cation attached to three ethylene glycol units. The backbones were either rigid vinyl-based or flexible siloxane-based (Fig. 6a). Walden plot analysis revealed strong decoupling in the rigid vinyl-based systems but minimal decoupling in the flexible siloxane-based PIL.⁴⁸ Moreover, the flexible siloxane-based PIL had the highest activation energy for transport, as determined by fitting the Arrhenius equation to the experimental data below T_g. In contrast, the PIL with a rigid backbone and rigid side group had the lowest activation energy.⁴⁸ The authors attributed this phenomenon to the reduced free volume in the flexible backbone system (Fig. 6b) that reduced transport pathways.⁴⁸

Fig. 6 (a) Chemical structures of PILs with different side groups and backbones. (b) Conductivity (top) and free volume (bottom) vs. T_g/T. Adapted with permission ref. 48. Copyright 2017 American Chemical Society.

As an alternative to varying the chemistry and rigidity of polymer side groups and backbones, adjusting molecular weight also can lead to decoupled ion transport. Fan et al. investigated the conduction properties of seven PILs with molecular weights ranging from 482 (monomer) to 160 400 g mol⁻¹.⁴⁴ The authors used a modified Walden plot analysis to determine that the system transformed to a strongly decoupled mechanism as the molecular weight increased.⁴⁴ The authors speculated that more linkages between repeating units in the polymeric cations introduced spatial constraints that hindered efficient chain packing and facilitated decoupling between ion transport and polymer segmental motion.⁴⁴ Similarly, Yang and Epps demonstrated that a binary blend system composed of a semiflexible SIC polymer – poly[lithium sulfonyl(trifluoromethane sulfonyl)imide methacrylate] (PLiMTFSI) – and a flexible, ion-conducting PEO derivative – poly(oligo-oxyethylene methyl ether methacrylate) had a shift in ion transport behavior with PLiMTFSI molecular weight.²⁰ As the molecular weight of PLiMTFSI increased from 7 to 52 kg mol⁻¹, the ionic conductivity increased by ∼100× due to a transition from transport being closely coupled to the polymer segmental dynamics (Vogel–Tammann–Fulcher [VTF]-like behavior) to more decoupled, Arrhenius-type behavior (Fig. 7). As the molecular weight further increased to 105 kg mol⁻¹, the conduction behavior remained Arrhenius-like, yet the conductivity dropped by almost an order of magnitude, indicating that other factors were also relevant, such as the impact of chain ends.²⁰ A key challenge lies in preserving the interconnectivity of the free volume network that facilitates ion motion. The dynamic and small-scale nature of these voids allows them to change size significantly with minor rearrangements,⁶¹ thereby impacting their size distribution and the corresponding ion transport efficiency.⁶²

Fig. 7 Ionic conductivity vs. 1000/T for PLiMTFSI – X/POEM blend electrolytes at (a) r = 5 : 1, (b) r = 10 : 1, (c) r = 20 : 1, and (d) r = 30 : 1; X represents the number-average molecular weight of the PLiMTFSI, r represents the molar ratio of the ether oxygen in POEM to Li⁺ in PLiMTFSI. Solid lines represent the Arrhenius fitting to the conductivity results, whereas dashed lines represent the VTF fitting to the conductivity data. Adapted with permission from ref. 20. Copyright 2024 American Chemical Society.

Ion–ion correlation and ion–polymer interaction

Understanding ion–ion correlations is crucial in the design of SPEs.⁶³ Cations that are coordinated exclusively by the polar groups of the polymer hosts are referred to as charge carriers, and their population strongly influences the overall conductivity.⁶⁴ In practical systems, cations may associate with one or more nearby anions to form ion pairs, triplets, or larger clustered species.⁶⁴ The relative populations of these coordination environments reflect a balance between electrostatic interactions among ions and the entropic constraints imposed by the polymer matrix.⁶⁴ Importantly, ion aggregates, particularly electrically neutral species such as ion pairs, do not participate in charge transport and therefore do not enhance conductivity.⁶⁴ The relation between ion diffusion and ionic conductivity can be well captured by the Nernst–Einstein (NE) equation [eqn (3)],⁶⁵in which D_σ is the charge diffusion coefficient estimated from the experimentally measured conductivity (σ), k_B is the Boltzmann constant, T is the absolute temperature, n is the free ion concentration, and e is the charge of the free ion.^47,65 D_σ is usually different from the self-diffusion coefficient of the ions, D_i, which can be obtained from solid-state nuclear magnetic resonance spectroscopy.⁴⁷ This phenomenon is expressed by the Haven ratio, H_R [eqn (4)], or the inverse of the Haven ratio, H_R⁻¹ [eqn (5)].⁶⁵H_R⁻¹ is a measure of correlated ionic motion. If ion–ion correlation decreases the conductivity, H_R⁻¹ will be below 1,^47,66 whereas H_R⁻¹ is above 1 in many superionic crystals and glasses.^67,68 A larger-than-one H_R⁻¹ also has been reported in PILs doped with a lithium salt, as shown in Fig. 8.⁶⁹

Fig. 8 Inverse Haven ratio as a function of Li⁺ loading. Adapted with permission from ref. 69. Copyright 2022 American Chemical Society.

As for ion movement, Ganesan and coworkers simulated ion transport mechanisms in poly(1-butyl-3-vinylimidazolium-hexafluorophosphate) PILs and found that anions move via a combination of intra- and inter-molecular ion hopping, which involved the continuous formation and breaking of ion associations.⁷⁰ These associations typically consisted of four tethered cationic monomers bound to two different polymer chains, and the anion mobility was directly linked to the average lifetimes of the ion associations.⁷⁰ The simulation results provided a theoretical foundation for the experimentally reported decoupled ion transport in PILs.^70–73 Furthermore, Hall and coworkers demonstrated that cation–anion correlations and the associated ion pair residence times decreased as the ion concentration increased using coarse-grained MD simulations.⁷⁴ Similar findings have been reported in experimental work, indicating that less correlated ion motion (cation–anion correlation) was found at higher ion concentrations.^75,76 Similarly, in precision polyethylene-based SIC ionomers reported by Winey and coworkers, Li⁺ travelled along percolating ion aggregates.⁷⁷ Although percolating pathways were present in these materials, the authors attributed the limited ion mobility and conductivity in these ionomers to the strong association between Li⁺ and the carboxylate (COO⁻) groups.⁷⁷ In a related work, precision SICs were prepared with a polyethylene backbone and a sulfonated phenyl group pendent on every fifth carbon.⁷⁸ The polymers were fully neutralized by a counterion X⁺ (Li⁺, Na⁺, or Cs⁺).⁷⁸ MD simulations revealed that the ionic groups nanophase separated from the polymer backbone to form percolating ionic aggregates, and the cations moved between sulfonate groups within these aggregates.⁷⁸

Beyond their direct impact on ion transport, ion–ion correlations and ion–polymer interactions also influence the morphology of the formed aggregates and the associated degree of decoupling. Winey and co-workers also studied a series of precisely segmented polyethylene-like SIC polymers containing sulfonate groups with Li⁺, Na⁺, Cs⁺, or NBu⁴⁺.⁷⁹ In the case of Li⁺-based SIC SPE, a bicontinuous gyroid morphology enabled higher ionic conductivity at a given temperature in comparison to its isotropic layered and hexagonal counterparts.⁷⁹ This enhanced performance was attributed to the three-dimensionally continuous, ion-conduction channels.^79,80 Additionally, Abbott and Lawson reported the self-assembly of ion aggregates through MD simulations and suggested that the ionic layer structure could be realized in polymers with well-defined side chains.⁸¹ They also showed that strong ionic interactions resulted in more rapid cation movement through aggregates versus systems with weaker ionic interactions.⁸¹ This conclusion was experimentally supported by McCloskey and co-workers.⁸²

It is worth noting that both thermodynamic parameters (e.g., temperature, composition) and kinetic factors (e.g., processing history) influence the equilibrium states and the resulting morphologies of ordered materials.^83–85 In ionomers with monomers neutralized with barium, gallium, zinc, or cobalt cations and a defined number of cations per chain (6 ions per chain), materials often adopt non-equilibrium structures.⁸⁶ The equilibrium state may be approached either by heating the ionomers to elevated temperatures for several days or by aging them at room temperature over several months.⁸⁶ During the transition from non-equilibrium to equilibrium morphologies, polymer chains dissociated and reorganized intramolecular interactions into intermolecular interactions, ultimately forming networked structures, as demonstrated in zinc ionomers.^86,87 This activated process proceeded more rapidly at higher temperatures.^86,87 In addition to experimental approaches, theoretical modeling and simulations have provided insight into structure–property relationships in SPEs. Frischknecht and Winey performed microsecond-long, atomistic MD simulations on a series of precise poly(ethylene-co-acrylic acid) ionomers neutralized with lithium.⁸⁸ Their work demonstrated that the ionic aggregates in these systems exhibited a range of morphologies, from isolated clusters to percolated networks.⁸⁸ The aggregates did not achieve a steady-state distribution within the simulation timeframe at a lower temperature (423 K), whereas at a higher temperature of 600 K, the increased mobility of the aggregates allowed them to reorganize and reach steady state after several hundred nanoseconds.⁸⁸ In another example of modeling proton transport within hydrogen-bonded networks, achieving a quantum-mechanical description of covalent and hydrogen-bond breaking and formation requires ion diffusion to be simulated within an equilibrated morphology obtained from density functional theory.⁸⁹ Overall, these examples underscore the complex interplay between structure (morphology), dynamics, and transport in polymer electrolytes and highlight the importance of considering both experimental and computational perspectives when interpreting ion transport behavior.

Conclusions and future directions

Decoupled, superionic conduction is ideal for unlocking next-generation battery technologies that meet increasingly demanding performance and safety requirements. Specifically, branched polymers and packing-frustrated systems with excess free volume can enable ion motion that is faster than the rate of polymer structural relaxation by creating low-energy-barrier conduction pathways. In parallel, SIC polymers with strong ion–ion correlations and tailored ion–polymer interactions can form percolating ionic aggregates to facilitate the formation of continuous transport channels. Beyond SPE systems, gel electrolytes, although outside the scope of this perspective, offer an alternative approach for tuning ion transport.^90–92 In these systems, the presence of a solvent within a polymer matrix can modulate solvent–cation interactions and alter chain motion, thereby reducing cation–polymer coordination⁹¹ or adjusting the effective width and connectivity of transport channels.⁹² The presence of solvent can increase Li⁺ mobility by lowering the energy cost required for Li⁺ to dissociate from polymer coordination sites.⁹⁰ For example, quantum chemistry studies have indicated that Li⁺ interacts strongly with sulfonate (SO₃⁻) groups, which are a functional motif commonly found in anion-containing polymers.^90,93 These strong Li⁺–SO₃⁻ interactions result in substantial activation barriers for Li⁺ dissociation and transport.⁹⁰ The introduction of solvent molecules can partially disrupt these associations, generating transient coordination environments in which Li⁺ is preferentially solvated by the solvent molecules with reduced energy barriers, thereby facilitating ion transport.⁹⁰ Together, these emerging design principles offer a promising roadmap for overcoming fundamental transport limitations in SPEs.

Future advances in solid polymer electrolytes will greatly benefit from strategies that enable precise control over polymer sequence and architecture and ion–ion/ion–polymer correlation to unlock rapid and highly selective transport. Sequence-defined polymers, in particular, provide a modular platform for fine-tuning the interconnectivity of transport pathways by precisely controlling the spatial arrangement of ionic and non-ionic segments.^94–97 Polymer rigidity and flexibility offer additional routes to enhanced ion transport. Polymers with rigid backbones and/or side groups can introduce packing frustration that increases accessible free volume, thereby facilitating ion hopping motion. In contrast, flexible polymer systems can achieve superionic transport by modulating ion concentration to adjust ion–ion correlations, which promotes the formation of favorable ion channels along the polymer matrix. Together, these mechanisms highlight the importance of controlling both polymer conformational freedom and ionic interactions when designing efficient transport networks. Furthermore, achieving an optimal balance between ionic conductivity and cation selectivity requires simultaneous control over polymer chemistry, salt concentration, ion coordination strength, coordination site distribution, phase behavior, and segmental dynamics. Rather than relying solely on iterative electrolyte synthesis and electrochemical testing, the integration of high-throughput synthesis and automated analysis could accelerate the discovery of promising compositions.^98–104

In summary, developing a fundamental understanding of the key design parameters that govern superionic conduction in SPEs is essential for advancing high-performance battery technologies. This perspective has highlighted critical insights regarding how polymer characteristics (chain-level architecture and sequence, ion dynamics, and ion–ion correlation environments) impact ion transport behavior. Continued progress in this field will depend on the ability to systematically tailor these features to overcome intrinsic transport limitations. Looking forward, future efforts could prioritize minimizing cation–anion correlations and leveraging labile ion-matrix interactions to promote facile hopping between solvation sites. Several computational studies have demonstrated that introducing a large size asymmetry between cations and anions, or increasing the degree of anion charge delocalization, can effectively enhance ion transport.^105,106 The integration of machine learning and data-driven modeling approaches will allow quantitative mapping of how phase behavior, ion dynamics, and ionic interaction strength collectively impact ion transport. As data from these efforts accumulate, they can be used to train predictive models to identify composition ranges that maximize ionic conductivity without sacrificing selective cation transport, enabling the efficient design of polymers with superior ion conduction characteristics.

Conflicts of interest

There are no conflicts of interest to declare.

Data availability

No primary research results, software, or code have been included, and no new data were generated or analyzed as part of this perspective.

Acknowledgements

M. Y. and T. H. E. gratefully acknowledge the United States Department of Energy Basic Energy Sciences (DOE BES; DE-SC0014458) for support during the writing of this manuscript.

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