The expansion of renewable energy and the growing number of electric vehicles and mobile devices are demanding improved and low-cost electrochemical energy storage. In order to meet the future needs for energy storage, novel material systems with high energy densities, readily available raw materials, and safety are required. Currently, lithium and lead mainly dominate the battery market, but apart from cobalt and phosphorous, lithium may show substantial supply challenges prospectively, as well. Therefore, the search for new chemistries will become increasingly important in the future, to diversify battery technologies. But which materials seem promising? Using a selection algorithm for the evaluation of suitable materials, the concept of a rechargeable, high-valent all-solid-state aluminum-ion battery appears promising, in which metallic aluminum is used as the negative electrode. On the one hand, this offers the advantage of a volumetric capacity four times higher (theoretically) compared to lithium analog. On the other hand, aluminum is the most abundant metal in the earth's crust. There is a mature industry and recycling infrastructure, making aluminum very cost efficient. This would make the aluminum-ion battery an important contribution to the energy transition process, which has already started globally. So far, it has not been possible to exploit this technological potential, as suitable positive electrodes and electrolyte materials are still lacking. The discovery of inorganic materials with high aluminum-ion mobility—usable as solid electrolytes or intercalation electrodes—is an innovative and required leap forward in the field of rechargeable high-valent ion batteries. In this review article, the constraints for a sustainable and seminal battery chemistry are described, and we present an assessment of the chemical elements in terms of negative electrodes, comprehensively motivate utilizing aluminum, categorize the aluminum battery field, critically review the existing positive electrodes and solid electrolytes, present a promising path for the accelerated development of novel materials and address problems of scientific communication in this field.

Here we have combined topological analysis, density functional theory (DFT) modeling, operando neutron diffraction, and machine learning algorithms within the comparative analysis of the known widely LiNiO 2 (LNO) and LiNi 0.8 Co 0.15 Al 0.05 O 2 (NCA) cathode materials. Full configurational spaces of the mentioned materials during delithiation were set using the topological approach starting from the 2 × 2 × 1 supercell (12 formula units in total) of the LNO structure (space group R3̅ m). Several types of the DFT models were applied for the structural relaxation of entries of the LNO configurational space (87 configurations) demonstrating a strong dependence of the results of optimization on the initial structure guess (at the latter delithiation stages) and on the Hubbard correction application (for the whole range of delithiation). Within the computationally easiest model considered for LNO, subsequent modeling of the NCA configurational space (20760 configurations) results in structural changes of the model cell that are well-consistent (relative errors <1.5% with respect to the lattice parameter values) with data of operando neutron diffraction experiments during charge−discharge cycling. In the scope of the machine learning approach, topology of Li layers and relative disposition of Li and Al in NCA structure are found to be the most important descriptors during the energy balance estimations.

No abstract

No abstract

We develop tools for extracting new information on crystal structures from crystallographic databases and show how to use these tools in the design of coordination compounds.

No abstract

Intermetallics contribute significantly to our current demand for high-performance functional materials. However, understanding their chemistry is still an open and debated topic, especially for complex compounds such as approximants and quasicrystals. In this work, targeted topological data mining succeeded in (i) selecting all known Mackay-type approximants, (ii) uncovering the most important geometrical and chemical factors involved in their formation, and (iii) guiding the experimental work to obtain a new binary Sc–Pd 1/1 approximant for icosahedral quasicrystals containing the desired cluster. Single-crystal X-ray diffraction data analysis supplemented by electron density reconstruction using the maximum entropy method, showed fine structural peculiarities, that is, smeared electron densities in correspondence to some crystallographic sites. These characteristics have been studied through a comprehensive density functional theory modeling based on the combination of point defects such as vacancies and substitutions. It was confirmed that the structural disorder occurs in the shell enveloping the classical Mackay cluster, so that the real structure can be viewed as an assemblage of slightly different, locally ordered, four shell nanoclusters. Results obtained here open up broader perspectives for machine learning with the aim of designing novel materials in the fruitful field of quasicrystals and their approximants. This might become an alternative and/or complementary way to the electronic pseudogap tuning, often used before explorative synthesis.

On the basis of DFT calculations, the Li-ion migration was analyzed for LiBH4, LiNH2, Li2NH, Li2BH4NH2, Li4BH4(NH2)3 and Li5(BH4)3NH complex hydrides by means of the nudged elastic band method. In...