Temperature effects on the ionic conductivity in concentrated alkaline electrolyte solutions

Mastering Proton Activities in Aqueous Batteries

Advanced aqueous batteries are promising solutions for grid energy storage. Compared with their organic counterparts, water-based electrolytes enable fast transport kinetics, high safety, low cost, and enhanced environmental sustainability. However, the presence of protons in the electrolyte, generated by the spontaneous ionization of water, may compete with the main charge-storage mechanism, trigger unwanted side reactions, and accelerate the deterioration of the cell performance. Therefore, it is of pivotal importance to understand and master the proton activities in aqueous batteries. This Perspective comments on the following scientific questions: Why are proton activities relevant? What are proton activities? What do we know about proton activities in aqueous batteries? How do we better understand, control, and utilize proton activities?

Generating Minimal Training Sets for Machine Learned Potentials

This Letter presents a novel approach for identifying uncorrelated atomic configurations from extensive datasets with a nonstandard neural network workflow known as random network distillation (RND) for training machine-learned interatomic potentials (MLPs). This method is coupled with a DFT workflow wherein initial data are generated with cheaper classical methods before only the minimal subset is passed to a more computationally expensive ab initio calculation. This benefits training not only by reducing the number of expensive DFT calculations required but also by providing a pathway to the use of more accurate quantum mechanical calculations. The method's efficacy is demonstrated by constructing machine-learned interatomic potentials for the molten salts KCl and NaCl. Our RND method allows accurate models to be fit on minimal datasets, as small as 32 configurations, reducing the required structures by at least 1 order of magnitude compared to alternative methods. This reduction in dataset sizes not only substantially reduces computational overhead for training data generation but also provides a more comprehensive starting point for active-learning procedures.

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).

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media

Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.

Unconventional Ferroelectricity with Quantized Polarizations in Ionic Conductors: High-Throughput Screening

Ferroelectricity is generally a displacive phenomenon within a unit cell in which ions are placed asymmetrically. In ionic conductors, ions can also be electrically displaced but by much longer distances. They are mostly nonpolar with symmetrical lattices due to the nondirectional character of ionic bondings. Here we propose that the combination of two such displacive modes may give rise to unconventional ferroelectricity with quantized polarizations, where even one local vacancy may induce giant polarization in ubiquitous ionic conductors. Such systems should be insulating with ion vacancies inclined to aggregate at one side. Our high-throughput screening combined with ab initio calculations provided 35 candidates, from which we select KSnS₄ and Na₄SnS₄ to show the existence of such long ion displacement ferroelectricity with a change in integer quantum number in polarizations during switching. The polarizations can be unprecedentedly large with a moderate density of ion vacancies that can be experimentally achieved via ion deintercalation.

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.

Training algorithm matters for the performance of neural network potential: A case study of Adam and the Kalman filter optimizers

One hidden yet important issue for developing neural network potentials (NNPs) is the choice of training algorithm. In this article, we compare the performance of two popular training algorithms, the adaptive moment estimation algorithm (Adam) and the extended Kalman filter algorithm (EKF), using the Behler-Parrinello neural network and two publicly accessible datasets of liquid water [Morawietz et al., Proc. Natl. Acad. Sci. U. S. A. 113, 8368-8373, (2016) and Cheng et al., Proc. Natl. Acad. Sci. U. S. A. 116, 1110-1115, (2019)]. This is achieved by implementing EKF in TensorFlow. It is found that NNPs trained with EKF are more transferable and less sensitive to the value of the learning rate, as compared to Adam. In both cases, error metrics of the validation set do not always serve as a good indicator for the actual performance of NNPs. Instead, we show that their performance correlates well with a Fisher information based similarity measure.

Artificial Intelligence Applied to Battery Research: Hype or Reality?

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries—a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

Importance of the Ion-Pair Lifetime in Polymer Electrolytes

Ion pairing is commonly considered as a culprit for the reduced ionic conductivity in polymer electrolyte systems. However, this simple thermodynamic picture should not be taken literally, as ion pairing is a dynamical phenomenon. Here we construct model poly(ethylene oxide)-bis(trifluoromethane)sulfonimide lithium salt systems with different degrees of ion pairing by tuning the solvent polarity and examine the relation between the cation-anion distinct conductivity σ+-d and the lifetime of ion pairs τ+- using molecular dynamics simulations. It is found that there exist two distinct regimes where σ+-d scales with 1/τ+- and τ+-, respectively, and the latter is a signature of longer-lived ion pairs that contribute negatively to the total ionic conductivity. This suggests that ion pairs are kinetically different depending on the solvent polarity, which renders the ion-pair lifetime highly important when discussing its effect on ion transport in polymer electrolyte systems.

Effect of temperature on concentrated electrolytes for advanced lithium ion batteries

Salt-concentrated electrolytes are emerging as promising electrolytes for advanced lithium ion batteries (LIBs) that can offer high energy density and improved cycle life. To further improve these electrolytes, it is essential to understand their inherent behavior at various operating conditions of LIBs. Molecular dynamics (MD) simulations are extensively used to study various properties of electrolytes and explain the associated molecular-level phenomena. In this study, we use classical MD simulations to probe the properties of the concentrated electrolyte solution of 3 mol/kg lithium hexafluorophosphate (LiPF₆) salt in the propylene carbonate solvent at various temperatures ranging from 298 to 378 K. Our results reveal that the properties such as ionic diffusivity and molar conductivity of a concentrated electrolyte are more sensitive to temperature compared to that of dilute electrolytes. The residence time analysis shows that temperature affects the Li⁺ ion solvation shell dynamics significantly. The effect of temperature on the transport and dynamic properties needs to be accounted carefully while designing better thermal management systems for batteries made with concentrated electrolytes to garner the advantages of these electrolytes.

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl

The in silico modeling of molten salts is critical for emerging "carbon-free" energy applications but is inhibited by the cost of quantum mechanically treating the high polarizabilities of molten salts. Here, we integrate configurational sampling using classical force fields with active learning to automate and accelerate the generation of Gaussian approximation potentials (GAP) for molten salts. This methodology reduces the number of expensive ab initio evaluations required for training set generation to O(100), enabling the facile parametrization of a molten LiCl GAP model that exhibits a 19 000-fold speedup relative to AIMD. The developed molten LiCl GAP model is applied to sample extended spatiotemporal scales, permitting new physical insights into molten LiCl's coordination structure as well as experimentally validated predictions of structures, densities, self-diffusion constants, and ionic conductivities. The developed methodology significantly lowers the barrier to the in silico understanding and design of molten salts across the periodic table.