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This study presents a data-driven battery emulator using long short-term memory deep learning models to predict the charge-discharge behaviour of lithium-ion batteries (LIBs). This study aimed to reduce the economic costs and time associated with the fabrication of large-scale automotive prototype batteries by emulating their performance using smaller laboratory-produced batteries. Two types of datasets were targeted: simulation data from the Dualfoil model and experimental data from liquid-based LIBs. These datasets were used to accurately predict the voltage profiles from the arbitrary inputs of various galvanostatic charge-discharge schedules. The results demonstrated high prediction accuracy, with the coefficient of determination scores reaching 0.98 and 0.97 for test datasets obtained from the simulation and experiments, respectively. The study also confirmed the significance of state-of-charge descriptors and inferred that a robust model performance could be achieved with as few as five charge-discharge training datasets. This study concludes that data-driven emulation using machine learning can significantly accelerate the battery development process, providing a powerful tool for reducing the time and economic costs associated with the production of large-scale prototype batteries., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Inexpensive and safe energy-storage batteries with high energy densities are in high demand (e.g., for electric vehicles and grid-level renewable energy storage). This study focused on using NaFeCl 4 , comprising ubiquitous elements, as an electrode material for all-solid-state sodium-ion batteries. Monoclinic NaFeCl 4 , expected to be the most resource-attractive Fe redox material, is also thermodynamically stable. The Fe 2+/3+ redox reaction of the monoclinic NaFeCl 4 electrode has a higher potential (3.45 V vs. Na/Na + ) than conventional oxide electrodes (e.g., Fe 2 O 3 with 1.5 V vs. Na/Na + ) because of the noble properties of chlorine. Additionally, NaFeCl 4 exhibits unusually high deformability (99% of the relative density of the pellet) upon uniaxial pressing (382 MPa) at 298 K. NaFeCl 4 operates at 333 K in an electrode system containing no electrolyte, thereby realizing next-generation all-solid-state batteries with high safety. A high energy density per positive electrode of 281 Wh kg -1 was achieved using only a simple powder press., (© 2024. The Author(s).)

Efforts to optimize known materials and enhance their performance are ongoing, driven by the advancements resulting from the discovery of novel functional materials. Traditionally, the search for and optimization of functional materials has relied on the experience and intuition of specialized researchers. However, materials informatics (MI), which integrates materials data and machine learning, has frequently been used to realize systematic and efficient materials exploration without depending on manual tasks. Nonetheless, the discovery of new materials using MI remains challenging. In this study, we propose a method for the discovery of materials outside the scope of existing databases by combining MI with the experience and intuition of researchers. Specifically, we designed a two-dimensional map that plots known materials data based on their composition and structure, facilitating researchers' intuitive search for new materials. The materials map was implemented using an autoencoder-based neural network. We focused on the conductivity of 708 lithium oxide materials and considered the correlation with migration energy (ME), an index of lithium-ion conductivity. The distribution of existing data reflected in the materials map can contribute to the development of new lithium-ion conductive materials by enhancing the experience and intuition of material researchers., (© 2023. Springer Nature Limited.)

Li-metal rechargeable batteries are an attractive option for devices that require an extremely high specific energy density, high robustness, and long-term durability, such as high-altitude platform stations. However, Li dendrite growth during charge-discharge cycling causes short-circuit problems. One technical solution is to form an intermediate layer between the Li metal and electrolyte. This interfacial layer should possess mechanical strength, electrochemical stability in the presence of Li, and Li-ion conductivity. In this study, the Li-ion conductivity of spinel-type LiAl 5 O 8 was investigated using first-principles density functional theory and force field molecular dynamics calculations. The calculation results confirmed that stoichiometric LiAl 5 O 8 compounds do not exhibit Li-ion conductivity, whereas off-stoichiometric compounds with excess Li show long-range Li-ion diffusion. The evaluated activation energy was 0.28 eV, which is as low as that of well-known fast Li-ion conductors, such as garnet-type Li 7 La 3 Zr 2 O 12 . However, the extrapolated Li-ion conductivity at 298 K was relatively low (~ 10 -6 S/cm) owing to the limited formation of migration pathways., (© 2022. The Author(s).)

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that leads to severe respiratory failure (RF). It is known that host exposure to viral infection triggers an iron-lowering response to mitigate pathogenic load and tissue damage. However, the association between host iron-lowering response and COVID-19 severity is not clear. This two-center observational study of 136 adult hospitalized COVID-19 patients analyzed the association between disease severity and initial serum iron, total iron-binding capacity (TIBC), and transferrin saturation (TSAT) levels. Serum iron levels were significantly lower in patients with mild RF than in the non-RF group; however, there were no significant differences in iron levels between the non-RF and severe RF groups, depicting a U-shaped association between serum iron levels and disease severity. TIBC levels decreased significantly with increasing severity; consequently, TSAT was significantly higher in patients with severe RF than in other patients. Multivariate analysis including only patients with RF adjusted for age and sex demonstrated that higher serum iron and TSAT levels were independently associated with the development of severe RF, indicating that inadequate response to lower serum iron might be an exacerbating factor for COVID-19.

Sequence data is now widely used to observe relationships among organisms. However, understanding structure of the qualitative data is challenging. Conventionally, the relationships are analysed using a dendrogram that estimates a tree shape. This approach has difficulty in verifying the appropriateness of the tree shape; rather, horizontal gene transfers and mating can make the shape of the relationship as networks. As a connection-free approach, principal component analysis (PCA) is used to summarize the distance matrix, which records distances between each combination of samples. However, this approach is limited regarding the treatment of information of sequence motifs; distances caused by different motifs are mixed up. This hides clues to figure out how the samples are different. As any bases may change independently, a sequence is multivariate data essentially. Hence, differences among samples and bases that contribute to the difference should be observed coincidentally. To archive this, the sequence matrix is transferred to boolean vector and directly analysed by using PCA. The effects are confirmed in diversity of Asiatic lion and human as well as environmental DNA. Resolution of samples and robustness of calculation is improved. Relationship of a direction of difference and causative nucleotides has become obvious at a glance.

Sodium ion batteries meet the demand for large-scale energy storage, such as in electric vehicles, due to the material abundance of sodium. In this report, nanotube-type Na 2 V 3 O 7 is proposed as a cathode material because of its fast sodium diffusivity, an important requirement for sodium ion batteries, through the investigation of ~4300 candidates via a high-throughput computation. High-rate performance was confirmed, showing ~65% capacity retention at a current density of 10C at room temperature, despite the large particle size of >5 μm. A good cycle performance of ca. 94% in capacity retention after 50 cycles was obtained owing to a small volumetric change of <0.4%.