Hiroki Moriwake

TL;DR: In this article, a machine learning technique is used to combine theoretical and experimental datasets to predict the conductivity of each composition at 373 K. The insights obtained show that an iterative combination of first-principle calculations and focused experiments can greatly accelerate the materials design process by enabling a wide compositional and structural phase space to be examined effi ciently.

TL;DR: In this article, van der Waals correction terms are introduced to better reproduce the layered graphite structure, and calculated formation energies of GICs AMC6, where AM = Li, Na, K, Rb, and Cs, are found to become less negative (less stable) as ion size decreases from Cs to Na as a result of weakening ionic bonding, until the formation energy of NaC6 becomes positive.

TL;DR: This study suggests that internal interfaces in LiCoO(2) strongly affect the battery voltage, battery capacity, and power density of this material, which is of particular concern if it is used in all-solid-state Li-ion batteries.

TL;DR: In this article, the authors used subangstrom resolution scanning transmission electron microscopy (STEM) imaging methods and spatially resolved electron energy loss spectroscopy (EELS) analysis to examine the local atomic structure of LLTO.