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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]

The timely arrival of novel materials plays a key role in bringing advances to society, as the pace at which major technological breakthroughs take place is usually dictated by the discovery rate at which novel materials are identified within chemical space. High‐throughput experimentation and computation strategy, now widely considered as a watershed in accelerating the discovery and optimization of novel materials in virtually every field, enables simultaneous screening, synthesis and characterization of large arrays of different material classes toward identification of the lead candidates for given system and targeted application. However, the ability to acquire data, through the continued advancement of automation platforms and workflows especially in the field of battery research and development, often outpaces the ability to optimally leverage obtained data for improved decision‐making. Closing this gap inevitably calls for adapted algorithms, development of reliable predictive models and enhanced integration with machine learning, deep learning, and artificial intelligence. This Review aims to highlight state‐of‐the‐art achievements along with an assessment of current and future challenges as well as resulting perspectives toward accelerated development of advanced battery electrolytes and their interfaces. [ABSTRACT FROM AUTHOR]

Antifreeze proteins (AFP) and antifreeze glycoproteins (AFGP) are synthesized by various organisms to enable their cells to survive low temperature environments like in the polar regions. The presence of antifreeze proteins leads to a temperature difference between the melting and freezing point of the solution known as thermal hysteresis. It is nowadays common knowledge that the antifreeze activity of AFPs is mainly determined by a short-range effect which includes a direct binding to the ice phase. Recently, experimental findings also revealed a long-range effect which implies a significant retardation of the water dynamics to facilitate the ice-binding process specifically for AFGPs. The aim of this work is to examine the dynamics of water molecules around different antifreeze protein residues by using atomistic molecular dynamics simulations. A prototype of AFP from antarctic notothenioids with the main subunit alanine-alanine-threonine (AAT) and a mutant (polyalanine) together with the residues of an antifreeze glycoprotein (AFGP) were simulated and compared with respect to their influence on the local water shell. The analysis of the water hydrogen bond characteristics and the dipolar relaxation times reveals a strong retardation effect of the water dynamics around the AFGP prototype. Our numerical results reveal the significant importance of polar units like threonine and disaccharides for the direct binding of water molecules in terms of hydrogen bonds and a significant retardation of water dynamics. In addition, a considerable change of the hydration dynamics is additionally observed in the presence of osmolytes like urea and hydroxyectoine. Our findings indicate that this effect is even more pronounced in the presence of kosmotropic osmolytes.