From Bulk to Interface: Solvent Exchange Dynamics and Their Role in Ion Transport and the Interfacial Model of Rechargeable Magnesium Batteries.

Multivalent battery chemistries have been explored in response to the increasing demand for high-energy rechargeable batteries utilizing sustainable resources. Solvation structures of working cations have been recognized as a key component in the design of electrolytes; however, most structure-property correlations of metal ions in organic electrolytes usually build upon favorable static solvation structures, often overlooking solvent exchange dynamics. We here report the ion solvation structures and solvent exchange rates of magnesium electrolytes in various solvents by using multimodal nuclear magnetic resonance (NMR) analysis and molecular dynamics/density functional theory (MD/DFT) calculations. These magnesium solvation structures and solvent exchange dynamics are correlated to the combined effects of several physicochemical properties of the solvents. Moreover, Mg2+ transport and interfacial charge transfer efficiency are found to be closely correlated to the solvent exchange rate in the binary electrolytes where the solvent exchange is tunable by the fraction of diluent solvents. Our primary findings are (1) most battery-related solvents undergo ultraslow solvent exchange coordinating to Mg2+ (with time scales ranging from 0.5 µs to 5 ms), (2) the cation transport mechanism is a mixture of vehicular and structural diffusion even at the ultraslow exchange limit (with faster solvent exchange leading to faster cation transport), and (3) an interfacial model wherein organic-rich regions facilitate desolvation and inorganic regions promote Mg2+ transport is consistent with our NMR, electrochemistry, and cryogenic X-ray photoelectron spectroscopy (cryo-XPS) results. This observed ultraslow solvent exchange and its importance for ion transport and interfacial properties necessitate the judicious selection of solvents and informed design of electrolyte blends for multivalent electrolytes.

Guiding maps of solvents for lithium-sulfur batteries via a computational data-driven approach.

Practical realization of lithium-sulfur batteries requires designing optimal electrolytes with controlled dissolution of polysulfides, high ionic conductivity, and low viscosity. Computational chemistry techniques enable tuning atomistic interactions to discover electrolytes with targeted properties. Here, we introduce ComBat (Computational Database for Lithium-Sulfur Batteries), a public database of ∼2,000 quantum-chemical and molecular dynamics properties for lithium-sulfur electrolytes composed of solvents spanning 16 chemical classes. We discuss the microscopic origins of polysulfide clustering and the diffusion mechanism of electrolyte components. Our findings reveal that polysulfide solubility cannot be determined by a single solvent property like dielectric constant. Rather, observed trends result from the synergistic effect of multiple factors, including solvent C/O ratio, fluorination degree, and steric hindrance effects. We propose binding energy as a proxy for Li+ dissociation, which is a property that impacts the ionic conductivity. The insights obtained in this work can serve as guiding maps to design optimal lithium-sulfur electrolyte compositions.

Non-polar ether-based electrolyte solutions for stable high-voltage non-aqueous lithium metal batteries.

The electrochemical instability of ether-based electrolyte solutions hinders their practical applications in high-voltage Li metal batteries. To circumvent this issue, here, we propose a dilution strategy to lose the Li+/solvent interaction and use the dilute non-aqueous electrolyte solution in high-voltage lithium metal batteries. We demonstrate that in a non-polar dipropyl ether (DPE)-based electrolyte solution with lithium bis(fluorosulfonyl) imide salt, the decomposition order of solvated species can be adjusted to promote the Li+/salt-derived anion clusters decomposition over free ether solvent molecules. This selective mechanism favors the formation of a robust cathode electrolyte interphase (CEI) and a solvent-deficient electric double-layer structure at the positive electrode interface. When the DPE-based electrolyte is tested in combination with a Li metal negative electrode (50 µm thick) and a LiNi0.8Co0.1Mn0.1O2-based positive electrode (3.3 mAh/cm2) in pouch cell configuration at 25 °C, a specific discharge capacity retention of about 74% after 150 cycles (0.33 and 1 mA/cm2 charge and discharge, respectively) is obtained.

MISPR: an open-source package for high-throughput multiscale molecular simulations.

Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure-Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure-property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions.

Identifying stable speciation in multi-component liquid solutions is fundamentally important to areas from electrochemistry to organic chemistry and biomolecular systems. Here we introduce a fully automated, high-throughput computational framework for the accurate prediction of stable species in liquid solutions by computing the nuclear magnetic resonance (NMR) chemical shifts. The framework automatically extracts and categorizes hundreds of thousands of atomic clusters from classical molecular dynamics simulations, identifies the most stable species in solution and calculates their NMR chemical shifts via density functional theory calculations. Additionally, the framework creates a database of computed chemical shifts for liquid solutions across a wide chemical and parameter space. We compare our computational results to experimental measurements for magnesium bis(trifluoromethanesulfonyl)imide Mg(TFSI)2 salt in dimethoxyethane solvent. Our analysis of the Mg2+ solvation structural evolutions reveals key factors that influence the accuracy of NMR chemical shift predictions in liquid solutions. Furthermore, we show how the framework reduces the performance of over 300 13C and 600 1H density functional theory chemical shift predictions to a single submission procedure.

Role of a Multivalent Ion-Solvent Interaction on Restricted Mg2+ Diffusion in Dimethoxyethane Electrolytes.

The diffusion behavior of Mg2+ in electrolytes is not as readily accessible as that from Li+ or Na+ utilizing PFG NMR, due to the low sensitivity, poor resolution, and rapid relaxation encountered when attempting 25Mg NMR. In MgTFSI2/DME solutions, "bound" DME (coordinating to Mg2+) and "free" DME (bulk) are distinguishable from 1H NMR. With the exchange rates between them obtained from 2D 1H EXSY NMR, we can extract the self-diffusivities of free DME and bound DME (which are equal to that of Mg2+) before the exchange occurs using PFG diffusion NMR measurements coupled with analytical formulas describing diffusion under two-site exchange. The high activation enthalpy for exhange (65-70 kJ/mol) can be explained by the structural change of bound DME as evidenced by its reduced C-H bond length. Comparison of the diffusion behaviors of Mg2+, TFSI-, DME, and Li+ reveals a relative restriction to Mg2+ diffusion that is caused by the long-range interaction between Mg2+ and solvent molecules, especially those with suppressed motions at high concentrations and low temperatures.