Correlations from Ion Pairing and the Nernst-Einstein Equation

Dynamic Ion Correlations and Ion-Pair Lifetimes in Aqueous Alkali Metal Chloride Electrolytes

Electrolytes are central to many technological applications, as well as life itself. The behavior and properties of electrolytes are often described in terms of ion pairs, whereby ions associate as either contact ion pairs (in which ions are "touching") solvent-separated ion pairs (in which ions' solvent shells overlap) or solvent-solvent-separated ion pairs (in which ions' solvent shells are distinct). However, this paradigm is generally restricted to statistically averaged descriptions of solution structure and ignores temporal behavior. Here we elucidate the time-resolved dynamics of these ion-ion interactions in aqueous metal chloride electrolytes using the partial van Hove correlation function, based on polarizable molecular dynamics simulations. Our results show that the existence and persistence of ion pairs in aqueous metal chloride electrolytes should not be assumed a priori, but in fact are ion specific features of the solution with lifetimes on subpicosecond time scales.

Cluster analysis as a tool for quantifying structure-transport properties in simulations of superconcentrated electrolyte

Using molecular dynamics simulations and graph-theory-based cluster analysis, we investigate the structure-transport properties of typical water-in-salt electrolytes. We demonstrate that ions exhibit distinct dynamics across different ionic clusters-namely, solvent-separated ion pairs (SSIPs), contact ion pairs (CIPs), and aggregates (AGGs). We assess the average proportions of various ionic species and their lifetimes. Our method reveals a dynamic decoupling of ion kinetics, with each species independently contributing to the overall molecular motion. This is evidenced by the fact that the total velocity autocorrelation function (VACF) and power spectrum can be expressed as a weighted sum of independent functions for each species. The experimental data on the ionic conductivity of the studied LiTFSI electrolytes align well with our theoretical predictions at various concentrations, based on the proportions and diffusion coefficients of free ions derived from our analysis. The insights gained into the solvation structures and dynamics of different ionic species enable us to elucidate the physical mechanisms driving ion transport in such superconcentrated electrolytes, providing a comprehensive framework for the future design and optimization of electrolytes.

Salt Effects on the Mechanical Properties of Ionic Conductive Polymer: A Molecular Dynamics Study

Functional polymers can be used as electrolyte and binder materials in solid-state batteries. This often requires performance targets in terms of both the transport and mechanical properties. In this work, a model ionic conductive polymer system, i.e., poly(ethylene oxide)-LiTFSI, was used to study the impact of salt concentrations on mechanical properties, including different types of elastic moduli and the viscoelasticity with both nonequilibrium and equilibrium molecular dynamics simulations. We found an encouragingly good agreement between experiments and simulations regarding Young's modulus, bulk modulus, and viscosity. In addition, we identified an intermediate salt concentration at which the system shows high ionic conductivity, high Young's modulus, and short elastic restoration time. Therefore, this study laid the groundwork for investigating ionic conductive polymer binders with self-healing functionality from molecular dynamics simulations.

Effect of K+ Force Fields on Ionic Conductivity and Charge Dynamics of KOH in Ethylene Glycol

Predicting ionic conductivity is crucial for developing efficient electrolytes for energy storage and conversion and other electrochemical applications. An accurate estimate of ionic conductivity requires understanding complex ion-ion and ion-solvent interactions governing the charge transport at the molecular level. Molecular simulations can provide key insights into the spatial and temporal behavior of electrolyte constituents. However, such insights depend on the ability of force fields to describe the underlying phenomena. In this work, molecular dynamics simulations were leveraged to delineate the impact of force field parameters on ionic conductivity predictions of potassium hydroxide (KOH) in ethylene glycol (EG). Four different force fields were used to represent the K+ ion. Diffusion-based Nernst-Einstein and correlation-based Einstein approaches were implemented to estimate the ionic conductivity, and the predicted values were compared with experimental measurements. The physical aspects, including ion-aggregation, charge distribution, cluster correlation, and cluster dynamics, were also examined. A force field was identified that provides reasonably accurate Einstein conductivity values and a physically coherent representation of the electrolyte at the molecular level.

Ion Transport in Polymer Electrolytes: Building New Bridges between Experiment and Molecular Simulation

ConspectusPolymer electrolytes constitute a promising type of material for solid-state batteries. However, one of the bottlenecks for their practical implementation lies in the transport properties, often including restricted Li+ self-diffusion and conductivity and low cationic transference numbers. This calls for a molecular understanding of ion transport in polymer electrolytes in which molecular dynamics (MD) simulation can provide both new physical insights and quantitative predictions. Although efforts have been made in this area and qualitative pictures have emerged, direct and quantitative comparisons between experiment and simulation remain challenging because of the lack of a unified theoretical framework to connect them.In our work, we show that by computing the glass transition temperature (Tg) of the model system and using the normalized inverse temperature 1000/(T - Tg + 50), the Li+ self-diffusion coefficient can be compared quantitatively between MD simulations and experiments. This allows us to disentangle the effects of Tg and the polymer dielectric environment on ion conduction in polymer electrolytes, giving rise to the identification of an optimal solvating environment for fast ion conduction.Unlike Li+ self-diffusion coefficients and ionic conductivity, the transference number, which describes the fraction of current carried by Li+ ions, depends on the boundary conditions or the reference frame (RF). This creates a non-negligible gap when comparing experiment and simulation because the fluxes in the experimental measurements and in the linear response theory used in MD simulation are defined in different RFs. We show that by employing the Onsager theory of ion transport and applying a proper RF transformation, a much better agreement between experiment and simulation can be achieved for the PEO-LiTFSI system. This further allows us to derive the theoretical expression for the Bruce-Vincent transference number in terms of the Onsager coefficients and make a direct comparison to experiments. Since the Bruce-Vincent method is widely used to extract transference numbers from experimental data, this opens the door to calibrating MD simulations via reproducing the Bruce-Vincent transference number and using MD simulations to predict the true transference number.In addition, we also address several open questions here such as the time-scale effects on the ion-pairing phenomenon, the consistency check between different types of experiments, the need for more accurate force fields used in MD simulations, and the extension to multicomponent systems. Overall, this Account focuses on building new bridges between experiment and simulation for quantitative comparison, warnings of pitfalls when comparing apples and oranges, and clarifying misconceptions. From a physical chemistry point of view, it connects to concentrated solution theory and provides a unified theoretical framework that can maximize the power of MD simulations. Therefore, this Account will be useful for the electrochemical energy storage community at large and set examples of how to approach experiments from theory and simulation (and vice versa).

Electrolyte Dependence of Li+ Transport Mechanisms in Small Molecule Solvents from Classical Molecular Dynamics

As demands on Li-ion battery performance increase, the need for electrolytes with high ionic conductivity and a high Li+ transference number (tLi) becomes crucial to boost power density. Unfortunately, tLi in liquid electrolytes is typically <0.5 due to Li+ migrating via a vehicular mechanism, whereby Li+ diffuses along with its solvation shell, making its diffusivity slower than the counteranion. Designing liquid electrolytes where the Li+ ion diffuses independently of its solvation shell is of significant interest to enhance the transference number. In this work, we elucidate how the properties of the solvent influence the Li+ transport mechanism. Using classical molecular dynamics simulations, we find that a vehicular mechanism can be increasingly preferred with a decreasing solvent viscosity and increasing interaction energy between the solvent and Li+. Thus, a weaker interaction energy can enhance tLi through a solvent-exchange mechanism, ultimately improving Li-ion battery performance. Finally, metadynamics simulations show that in electrolytes where a solvent-exchange mechanism is preferable, the energy barrier to changing the coordination environment of Li+ is much lower than in electrolytes where a vehicular mechanism dominates.

How Concerted Are Ionic Hops in Inorganic Solid-State Electrolytes?

Despite being fundamental to the understanding of solid-state electrolytes (SSEs), little is known on the degree of coordination between mobile ions in diffusive events, thus hindering a detailed comprehension and possible rational design of SSEs. Here, we introduce an unsupervised k-means clustering approach that is able to identify ion-hopping events and correlations between many mobile ions and apply it to a comprehensive ab initio MD database comprising several families of inorganic SSEs and millions of ionic configurations. It is found that despite two-body interactions between mobile ions being the largest, higher-order n-ion (2 < n) correlations are most frequent. Specifically, we prove a general exponential decaying law for the probability density function governing the number of concerted mobile ions. For the particular case of Li-based SSEs, it is shown that the average number of correlated mobile ions amounts to 10 ± 5 and that this result is practically independent of the temperature. Interestingly, our data-driven analysis reveals that fast-ionic diffusion strongly and positively correlates with ample hopping lengths and long hopping spans but not with high hopping frequencies and short interstitial residence times. Finally, it is shown that neglection of many-ion correlations generally leads to a modest overestimation of the hopping frequency that roughly is proportional to the average number of correlated mobile ions.

Ion transport mechanisms in pectin-containing EC-LiTFSI electrolytes

Using all-atom molecular dynamics simulations, we report the structure and ion transport characteristics of a new class of solid polymer electrolytes that contain the biodegradable and mechanically stable biopolymer pectin. We used highly conducting ethylene carbonate (EC) as a solvent for simulating lithium-trifluoromethanesulfonimide (LiTFSI) salt containing different weight percentages of pectin. Our simulations reveal that the pectin chains reduce the coordination number of lithium ions around their counterions (and vice versa) because of stronger lithium-pectin interactions compared to lithium-TFSI interactions. Furthermore, the pectin is found to promote smaller ionic aggregates over larger ones, in contrast to the results typically reported for liquid and polymer electrolytes. We observed that the loading of pectin in EC-LiTFSI electrolytes increases their viscosity (η) and relaxation timescales (τc), indicating higher mechanical stability, and, consequently, a decrease of the mean squared displacement, diffusion coefficient (D), and Nernst-Einstein conductivity (σNE). Interestingly, while the lithium diffusivities are related to the ion-pair relaxation timescales as D+ ∼ τc-3.1, the TFSI- diffusivities exhibit excellent correlations with ion-pair relaxation timescales as D- ∼ τc-0.95. On the other hand, the NE conductivities are dictated by distinct transport mechanisms and scales with ion-pair relaxation timescales as σNE ∼ τc-1.85.

Insights into the structure and Ion transport of pectin-[BMIM][PF6] electrolytes

We investigate the effect of pectin on the structure and ion transport properties of the room-temperature ionic liquid electrolyte 1-n-butyl-3-methylimidazolium hexafluorophosphate ([BMIM][PF6]) using molecular dynamics simulations. We find that pectin induces intriguing structural changes in the electrolyte that disrupt large ionic aggregates and promote the formation of smaller ionic clusters, which is a promising finding for ionic conductivity. Due to pectin in [BMIM][PF6] electrolytes, the diffusion coefficient of cations and anions is observed to decrease by a factor of four for a loading of 25 wt. % of pectin in [BMIM][PF6] electrolyte. A strong correlation between the ionic diffusivities (D) and ion-pair relaxation timescales (τc) is observed such that D ∼ τc-0.75 for cations and D ∼ τc-0.82 for anions. The relaxation timescale exponents indicate that the ion transport mechanisms in pectin-[BMIM][PF6] electrolytes are slightly distinct from those found in neat [BMIM][PF6] electrolytes (D∼τc-1). Since pectin marginally affects ionic diffusivities at the gain of smaller ionic aggregates and viscosity, our results suggest that pectin-ionic liquid electrolytes offer improved properties for battery applications, including ionic conductivity, mechanical stability, and biodegradability.

Polymer Architecture-Induced Trade-off between Conductivities and Transference Numbers in Salt-Doped Polymeric Ionic Liquids

Recent experiments have demonstrated that polymeric ionic liquids that share the same cation and anion but possess different architectures can exhibit markedly different conductivity and transference number characteristics when doped with lithium salt. In this study, we used atomistic molecular simulations on polymer chemistries inspired by the experiments to probe the mechanistic origins underlying the competition between conductivity and transference numbers. Our results indicate that the architecture of the polycationic ionic liquid plays a subtle but crucial role in modulating the anion-cation interactions, especially their dynamical coordination characteristics. Chemistries leading to longer-lived anion-cation coordinations relative to lithium-anion coordinations lead to lower conductivities and higher transference numbers. Our results suggest that higher conductivities are accompanied by lower transference numbers and vice versa, revealing that alternative approaches may need to be considered to break this trade-off in salt-doped polyILs.

Lithium transference in electrolytes with star-shaped multivalent anions measured by electrophoretic NMR

One approach for improving lithium transference in electrolytes is through the use of bulky multivalent anions. We have studied a multivalent salt containing a bulky star-shaped anion with a polyhedral oligomeric silsesquioxane (POSS) center and lithium counterions dissolved in a solvent. The charge on each anion, z-, is equal to -20. The self-diffusion coefficients of all species were measured by pulsed field gradient NMR (PFG-NMR). As expected, anion diffusion was significantly slower than cation diffusion. An approximate transference number, also referred to as the current fraction (measured by Bruce, Vincent and Watanabe method), was higher than those expected from PFG-NMR. However, the rigorously defined cation transference number with respect to the solvent velocity measured by electrophoretic NMR was negative at all salt concentrations. In contrast, the approximate transference numbers based on PFG-NMR and current fractions are always positive, as expected. The discrepancy between these three independent approaches for characterizing lithium transference suggests the presence of complex cation-anion interactions in solution. It is evident that the slow self-diffusion of bulky multivalent anions does not necessarily lead to an improvement of lithium transference.

Nanoscale Confinement Effects on Ionic Conductivity of Solid Polymer Electrolytes: The Interplay between Diffusion and Dissociation

Solid polymer electrolytes (SPEs) are attractive for next-generation lithium metal batteries but still suffer from low ionic conductivity. Nanostructured materials offer design concepts for SPEs with better performance. Using molecular dynamics simulation, we examine SPEs under nanoscale confinement, which has been demonstrated to accelerate the transport of neutral molecules such as water. Our results show that while ion diffusion indeed accelerates by more than 2 orders of magnitude as the channel diameter decreases from 15 to 2 nm, the ionic conductivity does not increase significantly in parallel. Instead, the ionic conductivity shows a nonmonotonic variation, with an optimal value above, but on the same order as, its bulk counterparts. This trend is due to enhanced ion association with decreasing channel size, which reduces the number of effective charge carriers. This effect competes with accelerated ion diffusion, leading to the nonmonotonicity in ion conductivity.

Constant chemical potential-quantum mechanical-molecular dynamics simulations of the graphene-electrolyte double layer

We present the coupling of two frameworks-the pseudo-open boundary simulation method known as constant potential molecular dynamics simulations (CμMD), combined with quantum mechanics/molecular dynamics (QMMD) calculations-to describe the properties of graphene electrodes in contact with electrolytes. The resulting CμQMMD model was then applied to three ionic solutions (LiCl, NaCl, and KCl in water) at bulk solution concentrations ranging from 0.5 M to 6 M in contact with a charged graphene electrode. The new approach we are describing here provides a simulation protocol to control the concentration of electrolyte solutions while including the effects of a fully polarizable electrode surface. Thanks to this coupling, we are able to accurately model both the electrode and solution side of the double layer and provide a thorough analysis of the properties of electrolytes at charged interfaces, such as the screening ability of the electrolyte and the electrostatic potential profile. We also report the calculation of the integral electrochemical double layer capacitance in the whole range of concentrations analyzed for each ionic species, while the quantum mechanical simulations provide access to the differential and integral quantum capacitance. We highlight how subtle features, such as the adsorption of potassium graphene or the tendency of the ions to form clusters contribute to the ability of graphene to store charge, and suggest implications for desalination.

Dynamic Heterogeneity of Solvent Motion and Ion Transport in Concentrated Electrolytes

Molecular-level understanding of the cation transference number t₊⁰, an important property that characterizes the transport of working cations, is critical to the bottom-up design of battery electrolytes. We quantify t₊⁰ in a model tetraglyme-based electrolyte using molecular dynamics simulation and the Onsager approach. t₊⁰ exhibits a concentration dependence in three distinct regimes: dilute, intermediate, and concentrated. The cluster approximation uncovers dominant correlations and dynamic heterogeneity in each regime. In the dilute regime, t₊⁰ decreases sharply as increasing numbers of solvent molecules become coordinated with Li⁺. The crossover to the intermediate regime, t₊⁰ ≈ 0, occurs when all solvent molecules become coordinated, and a plateau is obtained because anions enter the Li⁺ solvation shell, resulting in ion pairs that do not contribute to t₊⁰. Transference in concentrated electrolytes is dominated by the presence of cations in a variety of negatively charged and solvent-excluded clusters, resulting in t₊⁰ < 0.

Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations

The rate at which rechargeable batteries can be charged and discharged is governed by the selective transport of the working ions through the electrolyte. Conductivity, the parameter commonly used to characterize ion transport in electrolytes, reflects the mobility of both cations and anions. The transference number, a parameter introduced over a century ago, sheds light on the relative rates of cation and anion transport. This parameter is, not surprisingly, affected by cation-cation, anion-anion, and cation-anion correlations. In addition, it is affected by correlations between the ions and neutral solvent molecules. Computer simulations have the potential to provide insights into the nature of these correlations. We review the dominant theoretical approaches used to predict the transference number from simulations by using a model univalent lithium electrolyte. In electrolytes of low concentration, one can obtain a quantitative model by assuming that the solution is made up of discrete ion-containing clusters-neutral ion pairs, negatively and positively charged triplets, neutral quadruplets, and so on. These clusters can be identified in simulations using simple algorithms, provided their lifetimes are sufficiently long. In concentrated electrolytes, more clusters are short-lived and more rigorous approaches that account for all correlations are necessary to quantify transference. Elucidating the molecular origin of the transference number in this limit remains an unmet challenge.

Comparative Study of Lithium Halide-Based Electrolytes for Application in Lithium-Sulfur Batteries

Among the next-generation energy storage technologies, lithium-sulfur batteries are considered one of the most appealing solutions owing to their remarkable theoretical capacity. However, to become commercially competitive, there is a strong need to address some issues still characterizing this technology. One of the explored strategies is the optimization of the electrolyte formulation. To this aim, we compared 1,3-dioxolane/1,2-dimethoxyethane-based electrolytes containing two lithium halides, i.e., lithium bromide (LiBr) and lithium iodide (LiI), with lithium bis (trifluoromethane)sulfonylimide (LiTFSI) as a reference electrolyte. The obtained results show how the donicity of the lithium-salt anions might affect the solid electrolyte interphase stability and the lithium sulfide deposition morphology, therefore influencing the electrochemical performance of the cells. Among the tested electrolytes, the sulfur cell containing LiBr salt exhibited the best electrochemical performance maintaining a specific capacity of 900 mAh g−1 at C/4 and a stable trend along cycling at 1C with a specific capacity of about 770 mAh g−1 for 200 cycles.

Direct calculation of the ionic mobility in superionic conductors

We describe an approach based on non-equilibrium molecular dynamics (NEMD) simulations to calculate the ionic mobility of solid ion conductors such as solid electrolytes from first-principles. The calculations are carried out in finite slabs of the material, where an electric field is applied and the dynamic response of the mobile ions is measured. We compare our results with those obtained from diffusion calculations, under the non-interacting ion approximation, and with experiment. This method is shown to provide good quantitative estimates for the ionic mobilities of two silver conductors, $$\alpha$$ α -AgI and $$\alpha$$ α -RbAg$$_4$$ 4 I$$_5$$ 5 . In addition to being convenient and numerically robust, this method accounts for ion-ion correlations at a much lower computational cost than exact approaches.

Gelation, Clustering and Crowding in the Electrical Double Layer of Ionic Liquids

Understanding the bulk and interfacial properties of super-concentrated electrolytes, such as ionic liquids (ILs), has attracted significant attention lately for their promising applications in supercapacitors and batteries. Recently, McEldrew et al. developed a theory for reversible ion associations in bulk ILs, which accounted for the formation of all possible Cayley tree clusters and a percolating ionic network (gel). Here we adopt and develop this approach to understand the associations of ILs in the electrical double layer at electrified interfaces. With increasing charge of the electrode, the theory predicts a transition from a regime dominated by a gelled or clustered state to a crowding regime dominated by free ions. This transition from gelation to crowding is conceptually similar to the overscreening to crowding transition.

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.

Dynamical Ion Association and Transport Properties in PEO-LiTFSI Electrolytes: Effect of Salt Concentration

The association of ions describes the formation of ion species in electrolyte solutions and is strongly related to the salt concentration. However, the discussion of ion species and their transport is ambiguous in some studies on electrolyte materials due to the assumption of ideal solutions. Accordingly, in this work, molecular dynamics simulations are used to study ion association and transport properties of poly(ethylene)oxide (PEO)-lithium bis(trifluoromethanesulfonyl)imide electrolytes over a range of salt concentrations (r = [Li]/[EO]) from 0.01 to 0.20. Based on the analysis of the solvation environment and ion species, it is revealed that the distinct ion-ion correlations exist in two different characteristic areas, with a salt concentration of 0.10 as the limit. Below the critical concentration, the dynamic equilibrium between free ions and ion pairs is the most important process affecting the transport properties of electrolytes, but the process may have a minor influence on the applicability of the Nernst-Einstein relation. In concentrated solutions, a large number of ion pairs, triplets, and so forth appear in the electrolytes. The high-order ion clusters, with an average size of 3.95 at r = 0.20, are the main stable structures for transporting Li⁺, but the trapped free ions are the most abundant ion species. Meanwhile, the effect of salt concentrations on the average transport of ion clusters is to increase their average lifetime, but their transport rates remain unchanged. In addition, the coupling dynamics between ions and polymers is also discussed in order to gain a complete insight into the importance of salt concentrations.

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

Screening polymer electrolytes for batteries is extremely expensive due to the complex structures and slow dynamics. Here the authors develop a machine learning scheme to accelerate the screening and explore a space much larger than past studies.

Expanding the active charge carriers of polymer electrolytes in lithium-based batteries using an anion-hosting cathode

Ionic-conductive polymers are appealing electrolyte materials for solid-state lithium-based batteries. However, these polymers are detrimentally affected by the electrochemically-inactive anion migration that limits the ionic conductivity and accelerates cell failure. To circumvent this issue, we propose the use of polyvinyl ferrocene (PVF) as positive electrode active material. The PVF acts as an anion-acceptor during redox processes, thus simultaneously setting anions and lithium ions as effective charge carriers. We report the testing of various Li||PVF lab-scale cells using polyethylene oxide (PEO) matrix and Li-containing salts with different anions. Interestingly, the cells using the PEO-lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) solid electrolyte deliver an initial capacity of 108 mAh g⁻¹ at 100 μA cm⁻² and 60 °C, and a discharge capacity retention of 70% (i.e., 70 mAh g⁻¹) after 2800 cycles at 300 μA cm⁻² and 60 °C. The Li|PEO-LiTFSI|PVF cells tested at 50 μA cm⁻² and 30 °C can also deliver an initial discharge capacity of around 98 mAh g⁻¹ with an electrolyte ionic conductivity in the order of 10⁻⁵S cm⁻¹.

The energy content of secondary batteries is often limited by the charge carriers available in the system. Here, the authors employed an anion acceptor cathode for simultaneous use of electrolyte anions and cations as effective charge carriers in solid polymer electrolytes for lithium-based batteries.

Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Rechargeable batteries have become indispensable implements in our daily life and are considered a promising technology to construct sustainable energy systems in the future. The liquid electrolyte is one of the most important parts of a battery and is extremely critical in stabilizing the electrode-electrolyte interfaces and constructing safe and long-life-span batteries. Tremendous efforts have been devoted to developing new electrolyte solvents, salts, additives, and recipes, where molecular dynamics (MD) simulations play an increasingly important role in exploring electrolyte structures, physicochemical properties such as ionic conductivity, and interfacial reaction mechanisms. This review affords an overview of applying MD simulations in the study of liquid electrolytes for rechargeable batteries. First, the fundamentals and recent theoretical progress in three-class MD simulations are summarized, including classical, ab initio, and machine-learning MD simulations (section 2). Next, the application of MD simulations to the exploration of liquid electrolytes, including probing bulk and interfacial structures (section 3), deriving macroscopic properties such as ionic conductivity and dielectric constant of electrolytes (section 4), and revealing the electrode-electrolyte interfacial reaction mechanisms (section 5), are sequentially presented. Finally, a general conclusion and an insightful perspective on current challenges and future directions in applying MD simulations to liquid electrolytes are provided. Machine-learning technologies are highlighted to figure out these challenging issues facing MD simulations and electrolyte research and promote the rational design of advanced electrolytes for next-generation rechargeable batteries.

Electric-Field-Induced Spatially Dynamic Heterogeneity of Solvent Motion and Cation Transference in Electrolytes

While electric fields primarily result in migration of charged species in electrolytic solutions, the solutions are dynamically heterogeneous. Solvent molecules within the solvation shells of the cation will be dragged by the field while free solvent molecules will not. We combine electrophoretic NMR measurements of ion and solvent velocities under applied electric fields with molecular dynamics simulations to interrogate different solvation motifs in a model liquid electrolyte. Measured values of the cation transference number (t_{+}^{0}) agree quantitatively with simulation-based predictions over a range of electrolyte concentrations. Solvent-cation interactions strongly influence the concentration-dependent behavior of t_{+}^{0}. We identify a critical concentration at which most of the solvent molecules lie within solvation shells of the cations. The dynamic heterogeneity of solvent molecules is minimized at this concentration where t_{+}^{0} is approximately equal to zero.

Fast evaluation technique for the shear viscosity and ionic conductivity of electrolyte solutions

With the growing need to obtain ideal materials for various applications, there is an increasing interest in computational methods to rapidly and accurately search for materials. Molecular dynamics simulation is one of the successful methods used to investigate liquid electrolytes with high transport properties applied in lithium-ion batteries. However, further reduction in computational cost is required to find a novel material with the desired properties from a large number of combinations. In this study, we demonstrate an effective fast evaluation technique for shear viscosity and ionic conductivity by molecular dynamics simulation for an exhaustive search of electrolyte materials with high transport properties. The proposed model was combined with a short-time correlation function of the stress tensor and empirical relationships to address the issues of inefficient and uncertain evaluation by conventional molecular dynamics methods. Because we focus on liquid electrolytes consisting of organic solvents and lithium salts, our model requires dissociation ratio and effective diffusion size of lithium salts. Our method is applied to search for the compositional combinations of electrolytes with superior transport properties even at low temperatures. These results correlate well with experimental results.

Transference Number in Polymer Electrolytes: Mind the Reference-Frame Gap

The transport coefficients, in particular the transference number, of electrolyte solutions are important design parameters for electrochemical energy storage devices. The recent observation of negative transference numbers in PEO–LiTFSI under certain conditions has generated much discussion about its molecular origins, by both experimental and theoretical means. However, one overlooked factor in these efforts is the importance of the reference frame (RF). This creates a non-negligible gap when comparing experiment and simulation because the fluxes in the experimental measurements of transport coefficients and in the linear response theory used in the molecular dynamics simulation are defined in different RFs. In this work, we show that, by applying a proper RF transformation, a much improved agreement between experimental and simulation results can be achieved. Moreover, it is revealed that the anion mass and the anion–anion correlation, rather than ion aggregates, play a crucial role for the reported negative transference numbers.

Superionic states formation in group III oxides irradiated with ultrafast lasers

After ultrafast laser irradiation, a target enters a poorly explored regime where physics of a solid state overlaps with plasma physics and chemistry, creating an unusual synergy—a warm dense matter state (WDM). We study theoretically the WDM kinetics and chemistry in a number of group III-metal oxides with highly excited electronic system. We employ density functional theory to investigate a possibility of nonthermal transition of the materials into a superionic state under these conditions. Atomic and electronic properties of the materials are analyzed during the transitions to acquire insights into physical mechanisms guiding such transformations.

The solvation structure, transport properties and reduction behavior of carbonate-based electrolytes of lithium-ion batteries

Despite the extensive employment of binary/ternary mixed-carbonate electrolytes (MCEs) for Li-ion batteries, the role of each ingredient with regards to the solvation structure, transport properties, and reduction behavior is not fully understood. Herein, we report the atomistic modeling and transport property measurements of the Gen2 (1.2 M LiPF₆ in ethylene carbonate (EC) and ethyl methyl carbonate (EMC)) and EC-base (1.2 M LiPF₆ in EC) electrolytes, as well as their mixtures with 10 mol% fluoroethylene carbonate (FEC). Due to the mixing of cyclic and linear carbonates, the Gen2 electrolyte is found to have a 60% lower ion dissociation rate and a 44% faster Li⁺ self-diffusion rate than the EC-base electrolyte, while the total ionic conductivities are similar. Moreover, we propose for the first time the anion–solvent exchange mechanism in MCEs with identified energetic and electrostatic origins. For electrolytes with additive, up to 25% FEC coordinates with Li⁺, which exhibits a preferential reduction that helps passivate the anode and facilitates an improved solid electrolyte interphase. The work provides a coherent computational framework for evaluating mixed electrolyte systems.

The different roles of the anion, cyclic and linear carbonates, and additive in mixed-carbonate electrolytes are revealed. The anion–solvent exchange mechanism and factors influencing the solid electrolyte interphase (SEI) formation are deciphered.

Spectral Denoising for Accelerated Analysis of Correlated Ionic Transport

Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive, as one cannot rely on the affordable mean square displacement approach. We use spectral decomposition of the short-time ionic displacement covariance to learn a set of diffusion eigenmodes that encode the correlation structure and form a basis for analyzing the ionic trajectories. This allows systematic reduction of the uncertainty and accelerate computations of ionic conductivity in systems with a steady-state correlation structure. We provide mathematical and numerical proofs of the method's robustness and demonstrate it on realistic electrolyte materials.

Abnormal Enhanced Free Ions of Ionic Liquids Confined in Carbon Nanochannels

Revealing the structure and behavior of confined ionic liquids (ILs) is essential for their applications in green chemical processes. Here, we explore the electroconductivity (σ) and ionic correlation of imidazole ILs confined in graphene nanochannels via joint molecular dynamics simulation and theoretical analysis. The ideal and actual σ of ILs are first calculated, showing a growing tendency and up to the bulk value as the nanochannel size ranges from 1 to 10 nm. To account for the ionic correlation, the ionicity was determined by the ratio of the actual to ideal σ, reflecting the average fraction of free ions in the confined ILs. Amazingly, the ionicity of all three ILs shows an abnormal changing tendency, which first increases and reaches the maximum at 2 nm and then decreases to the bulk value. The conformational analysis, pair dissociating energy, and residence time are further obtained, proving that the abnormal enhanced ionicity should be attributed to the structure reconstruction of ILs near the graphene wall. The analytical model of ionicity herein can guide the rational design of efficient IL-based nanoporous electrodes and solid catalysts.

Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes

Electrolytes play an important role in a plethora of applications ranging from energy storage to biomaterials. Notwithstanding this, the structure of concentrated electrolytes remains enigmatic. Many theoretical approaches attempt to model the concentrated electrolyte by introducing the idea of ion pairs, with ions either being tightly "paired" with a counter-ion or "free" to screen charge. In this study, we reframe the problem into the language of computational statistics and test the null hypothesis that all ions share the same local environment. Applying the framework to molecular dynamics simulations, we find that this null hypothesis is not supported by data. Our statistical technique suggests the presence of two distinct local ionic environments at intermediate concentrations, whose differences surprisingly originate in like charge correlations rather than unlike charge attraction. Through considering the effect of these "aggregated" and "non-aggregated" states on bulk properties including effective ion concentration and dielectric constant, we identify a scaling relation between the effective screening length and theoretical Debye length, which applies across different dielectric constants and ion concentrations.

Correlated Ion Transport and the Gel Phase in Room Temperature Ionic Liquids

Here we present a theory of ion aggregation and gelation of room temperature ionic liquids (RTILs). Based on it, we investigate the effect of ion aggregation on correlated ion transport-ionic conductivity and transference numbers-obtaining closed-form expressions for these quantities. The theory depends on the maximum number of associations a cation and anion can form and the strength of their association. To validate the presented theory, we perform molecular dynamics simulations on several RTILs and a range of temperatures for one RTIL. The simulations indicate the formation of large clusters, even percolating through the system under certain circumstances, thus forming a gel, with the theory accurately describing the obtained cluster distributions in all cases. However, based on the strength and lifetime of associations in the simulated RTILs, we expect free ions to dominate ionic conductivity despite the presence of clusters, and we do not expect the percolating cluster to trigger structural arrest in the RTIL.

Ionic Mobility in Ion-Exchange Membranes

Membrane technologies are widely demanded in a number of modern industries. Ion-exchange membranes are one of the most widespread and demanded types of membranes. Their main task is the selective transfer of certain ions and prevention of transfer of other ions or molecules, and the most important characteristics are ionic conductivity and selectivity of transfer processes. Both parameters are determined by ionic and molecular mobility in membranes. To study this mobility, the main techniques used are nuclear magnetic resonance and impedance spectroscopy. In this comprehensive review, mechanisms of transfer processes in various ion-exchange membranes, including homogeneous, heterogeneous, and hybrid ones, are discussed. Correlations of structures of ion-exchange membranes and their hydration with ion transport mechanisms are also reviewed. The features of proton transfer, which plays a decisive role in the membrane used in fuel cells and electrolyzers, are highlighted. These devices largely determine development of hydrogen energy in the modern world. The features of ion transfer in heterogeneous and hybrid membranes with inorganic nanoparticles are also discussed.

Theory of cell membrane interaction with glass

There are three regimes of cell membrane interaction with glass: Tight and loose adhesion, separated by repulsion. Explicitly including hydration, this paper evaluates the pressure between the surfaces as functions of distance for ion correlation and ion-screened electrostatics and electromagnetic fluctuations. The results agree with data for tight adhesion energy (0.5-3 vs 0.4-4 mJ/m^{2}), detachment pressure (7.9 vs. 9 MPa), and peak repulsion (3.4-7.5 vs. 5-10 kPa), also matching the repulsion's distance dependence on renormalization by steric pressure mainly from undulations.