Enabling all-solid-state Li-ion batteries requires solid electrolytes with high Li ionic conductivity and good electrochemical stability.F ollowing recent experimental reports of Li 3 YCl 6 and Li 3 YBr 6 as promising new solid electrolytes,weused first principles computation to investigate the Li-ion diffusion, electrochemical stability,a nd interface stability of chloride and bromide materials and elucidated the origin of their high ionic conductivities and good electrochemical stabilities.Chloride and bromide chemistries intrinsically exhibit lowmigration energy barriers,wide electrochemical windows,a nd are not constrained to previous design principles for sulfide and oxide Li-ion conductors,allowing for muchg reater freedom in structure,c hemistry,c omposition, and Li sublattice for developing fast Li-ion conductors.O ur study highlights chloride and bromide chemistries as apromising new researchdirection for solid electrolytes with high ionic conductivity and good stability.All-solid-state lithium-ion batteries (ASBs) with inorganic lithium solid electrolytes (SEs) are regarded as promising next-generation energy storage devices.ASBs solve the safety issue caused by the flammability of organic liquid electrolyte and potentially provide higher energy density with Li metal anode and high-voltage cathode materials. [1] However,i th as been ag reat challenge to develop solid-state Li-ion conductors with high Li + conductivity at room temperature comparable to that of liquid electrolytes and with good electrochemical stability for Li-ion batteries with avoltage of > 4V .C urrent research efforts on solid-state Li-ion conductors focus mostly on oxides and sulfides. [1a,b,2] Unfortunately, oxide and sulfide chemistries have an undesirable trade-off between ionic conductivity and stability.S ulfide-based solidstate Li-ion conductors such as Li 10 GeP 2 S 12 (LGPS) andSupportinginformation and the ORCID identification number(s) for the author(s) of this article can be found under: https://doi.Figure 3. Calculated thermodynamics intrinsic electrochemical windows of Li-M-X ternary fluorides, chlorides, bromides, iodides, oxides, and sulfides. Mi sametal cation at its highest commonv alence state.

Potassium‐ion batteries (KIBs) are very promising alternatives to lithium‐ion batteries (LIBs) for large‐scale energy storage. However, traditional carbon anode materials usually show poor performance in KIBs due to the large size of K ions. Herein, a carbonization‐etching strategy is reported for making a class of sulfur (S) and oxygen (O) codoped porous hard carbon microspheres (PCMs) material as a novel anode for KIBs through pyrolysis of the polymer microspheres (PMs) composed of a liquid crystal/epoxy monomer/thiol hardener system. The as‐made PCMs possess a porous architecture with a large Brunauer–Emmett–Teller surface area (983.2 m2 g−1), an enlarged interlayer distance (0.393 nm), structural defects induced by the S/O codoping and also amorphous carbon nature. These new features are important for boosting potassium ion storage, allowing the PCMs to deliver a high potassiation capacity of 226.6 mA h g−1 at 50 mA g−1 over 100 cycles and be displaying high stability by showing a potassiation capacity of 108.4 mA h g−1 over 2000 cycles at 1000 mA g−1. The density functional theory calculations demonstrate that S/O codoping not only favors the adsorption of K to the PCMs electrode but also reduces its structural deformation during the potassiation/depotassiation. The present work highlights the important role of hierarchical porosity and S/O codoping in potassium storage.

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Quality assessment is essential for the computational prediction and design of RNA tertiary structures. To date, several knowledge-based statistical potentials have been proposed and proved to be effective in identifying native and near-native RNA structures. All these potentials are based on the inverse Boltzmann formula, while differing in the choice of the geometrical descriptor, reference state, and training dataset. Via an approach that diverges completely from the conventional statistical potentials, our work explored the power of a 3D convolutional neural network (CNN)-based approach as a quality evaluator for RNA 3D structures, which used a 3D grid representation of the structure as input without extracting features manually. The RNA structures were evaluated by examining each nucleotide, so our method can also provide local quality assessment. Two sets of training samples were built. The first one included 1 million samples generated by high-temperature molecular dynamics (MD) simulations and the second one included 1 million samples generated by Monte Carlo (MC) structure prediction. Both MD and MC procedures were performed for a non-redundant set of 414 RNAs. For two training datasets (one including only MD training samples and the other including both MD and MC training samples), we trained two neural networks, named RNA3DCNN_MD and RNA3DCNN_MDMC, respectively. The former is suitable for assessing near-native structures, while the latter is suitable for assessing structures covering large structural space. We tested the performance of our method and made comparisons with four other traditional scoring functions. On two of three test datasets, our method performed similarly to the state-of-the-art traditional scoring function, and on the third test dataset, our method was far superior to other scoring functions. Our method can be downloaded from https://github.com/lijunRNA/RNA3DCNN.