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Although data-driven optimization has been recognized as a useful approach for the advancement of liquid battery electrolytes, a high quality and large dataset is essential to avoid bias in the interpretation of results. In this work, we present the newly designed and developed platform which comprises an automated high-throughput experimentation (HTE) modular system coupled with the Liquid Electrolyte Composition Analysis (LECA) package for the data-driven modeling and analysis of ionic conductivity as a vital bulk electrolyte property. The LECA package combines popular machine learning libraries into a simplified workflow enabling easy, semi-automated processing and analysis of HTE acquired data. The package facilitates the parallel training, cross-validation and uncertainty estimation of Linear Regression, Random Forest, Neural Network and Gaussian Process Regression models. By comparatively scoring model prediction accuracy, the LECA package identifies the best performing model architecture(s) and applies them to find electrolyte compositions which maximize ionic conductivity. Overall, this comprehensive and versatile platform with automated experiments and data-driven analysis paves the way for more efficient and insightful research in liquid battery electrolyte development. [ABSTRACT FROM AUTHOR]

Liquid electrolyte design and modelling is an essential part of the development of improved lithium ion batteries. For mixed organic carbonates (ethylene carbonate (EC) and ethyl–methyl carbonate (EMC) mixtures)-based electrolytes with LiPF6 as salt, we have compared a polarizable force field with the standard non-polarizable force field with and without charge rescaling to model the structural and dynamic properties. The result of our molecular dynamics simulations shows that both polarizable and non-polarizable force fields have similar structural factors, which are also in agreement with X-ray diffraction experimental results. In contrast, structural differences are observed for the lithium neighborhood, while the lithium–anion neighbourhood is much more pronounced for the polarizable force field. Comparison of EC/EMC coordination statistics with Fourier transformed infrared spectroscopy (FTIR) shows the best agreement for the polarizable force field. Also for transport quantities such as ionic conductivities, transference numbers, and viscosities, the agreement with the polarizable force field is by far better for a large range of salt concentrations and EC : EMC ratios. In contrast, for the non-polarizable variants, the dynamics are largely underestimated. The excellent performance of the polarizable force field is explored in different ways to pave the way to a realistic description of the structure–dynamics relationships for a wide range of salt and solvent compositions for this standard electrolyte. In particular, we can characterize the distinct correlation terms between like and unlike ions, relate them to structural properties, and explore to which degree the transport in this electrolyte is mass or charge limited. [ABSTRACT FROM AUTHOR]

We study the solvation and the association properties of ion pairs in aqueous dimethyl sulfoxide (DMSO) solution by atomistic molecular dynamics (MD) simulations. The ion pair is composed of two lithium and a single sulfonated diphenyl sulfone ion whose properties are studied under the influence of different DMSO concentrations. For increasing mole fractions of DMSO, we observe a non-ideal behavior of the solution as indicated by the derivatives of the chemical activity. Our findings are complemented by dielectric spectra, which also verify a complex DMSO–water mixing behavior. In agreement with these results, further simulation outcomes reveal an aqueous homoselective solvation of the ion species which fosters the occurrence of pronounced ion association constants at higher DMSO mole fractions. The consequences of this finding are demonstrated by lower ionic conductivities for increasing concentrations of DMSO. [ABSTRACT FROM AUTHOR]