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Solid electrolytes with high Mg-ion conductivity are required to develop solid-state Mg-ion batteries. The migration energies of the Mg 2+ ions of 5,576 Mg compounds tabulated from the inorganic crystal structure database (ICSD) were evaluated via high-throughput calculations. Among the computational results, we focused on the Mg 2+ ion diffusion in Mg 0.6 Al 1.2  Si 1.8 O 6 , as this material showed a relatively low migration energy for Mg 2+ and was composed solely of ubiquitous elements. Furthermore, first-principles molecular dynamics calculations confirmed a single-phase Mg 2+ ion conductor. The bulk material with a single Mg 0.6 Al 1.2 Si 1.8 O 6 phase was successfully prepared using the sol-gel method. The relative density of the sample was 81%. AC impedance measurements indicated an electrical conductivity of 1.6 × 10 −6 Scm −1 at 500°C. The activation energy was 1.32 eV, which is comparable to that of monoclinic-type Mg 0.5 Zr 2 (PO 4 ) 3 .

Cellulose, a highly versatile material, faces challenges in processing due to its limited solubility in common solvents. Ionic liquids have been found to possess high solvating capacities for cellulose. However, the experimental development of ionic liquids with optimal cellulose solubilities remains a time-consuming trial-and-error process. In this work, a virtual molecular library containing billions of potentially de novo ionic liquid candidates has been generated utilizing Monte Carlo tree search and recurrent neural network techniques. The library is subsequently screened through two predictive machine learning models, which have been pre-trained for predicting cellulose solubility and melting point of ionic liquids. The promising candidates were further validated and screened using the Conductor-like Screening Model for Real Solvents (COSMO-RS) model. Our work offers an efficient workflow and virtual molecular library, which should facilitate theoretical and experimental development of novel ionic liquids. Scientific Contribution We propose a novel approach to organic ion generation by integrating Monte Carlo tree search with recurrent neural network techniques. Utilizing this approach a virtual molecular library of billions of potential ionic liquids has been generated. Additionally, two predictive machine learning models were developed to predict cellulose solubility and melting points of ionic liquids. This work offers a comprehensive and efficient workflow to aid in the development of new ionic liquids.

The design of rare earth (RE) bearing steels requires a thorough understanding of the formation tendency of RE involved phases in steels, while searching for binary and ternary compounds with a wide variety of composition and permutation need a remarkable amount of experimentation which is nearly infeasible. In the present work, we perform a thorough search for the RE-contained compounds in steels by a data-driven high-throughput computational approach. The search results indicate that RE may react with O and N to form a large amounts of oxide and nitride inclusions, while only Y participate in the formation of sulfide inclusion Y 2 MnS 4 and Y 2 CaS 4 . For the case of ternary compounds in Fe-based solid solution, it is found that RE prefers to form ternary phases with the non-metallic elements, i.e., B, C, O, P and Si, and only Y is found to combine with metal Cr to form YCr 4 Fe 8 . Finally, our screen suggests that RE can participate in the formation of the nano-scale precipitates of κ-carbides, L1 2 precipitates and B2 precipitates, but MC and M 2 C carbides.

Fragment-based ligand design approaches, such as the multi-copy simultaneous search (MCSS) methodology, have proven to be useful tools in the search for novel therapeutic compounds that bind pre-specified targets of known structure. MCSS offers a variety of advantages over more traditional high-throughput screening methods, and has been applied successfully to challenging targets. The methodology is quite general and can be used to construct functionality maps for proteins, DNA, and RNA. In this review, we describe the main aspects of the MCSS method and outline the general use of the methodology as a fundamental tool to guide the design of de novo lead compounds. We focus our discussion on the evaluation of MCSS results and the incorporation of protein flexibility into the methodology. In addition, we demonstrate on several specific examples how the information arising from the MCSS functionality maps has been successfully used to predict ligand binding to protein targets and RNA.

A high-throughput and data-mining search for rare-earth-free permanent magnets is reported for materials containing a 3d and p-element of the Periodic Table. Three of the most promising compounds, FeC, MnMoB, and MnWB, were investigated in detail by ab initio electronic structure theory coupled to atomistic spin-dynamics. For these systems doping protocols were also investigated and, in particular, (FeX)C (X = Mn, Cr, V, and Ti), MnXB (X = Mo and W) along with Mn(XY)B (X,Y = Mo, W, Ta, Cr) are suggested here as promising candidates for applications as permanent magnets.