High-performance permanent magnets are indispensable for everyday life (hard disk drives, hybrid and electric vehicles, wind power generators, and so on). Recently, the application is expanded to e.g. small robots and drones, and their demand is increasing year by year. In this article, we begin with the history of the development of permanent magnets, and revisit the invention of neodymium magnets from theoretical viewpoint. We then discuss how computational science and data-driven approaches are used in the development of permanent magnets.

Since the development of the aberration corrector, atomic-resolution scanning transmission electron microscopy has become a versatile tool for investigating local atomic structures in various fields. However, the accessible local atomic structure information is limited to the projected two dimensions along the observation orientation. This review will introduce recent progress in STEM depth sectioning with large-angle illumination.

Huge amount of low-temperature waste heat emitted from various shaped objects is left unused. For efficient conversion of such waste heat to electricity, flexible thermoelectrics attracts a lot of attention. A lot of van der Waals interfaces are present in flexible materials, and the basic understanding on how the van der Waals interfaces affect the thermoelectric properties is crucial for the enhancement of their thermoelectric performance. Here we present the short summary of our systematical studies on the thermoelectric properties of single walled carbon nanotubes as one of models for thermoelectrics with van der Waals interfaces, and discuss future potential and perspective.

This study aims to develop wireless multi-modal and multi-tasking flexible sensor sheets. To realize multi-modal flexible sensors, a variety of sensors using low-cost solution-based process are proposed to integrate on flexible films. In particular, by attaching the sensor sheet to human, monitoring of continuous vital signals is demonstrated. For multi-tasking system to make X-in-1 sensor, reservoir computing, which is one of machine learning techniques, is applied to a flexible sensor. As a proof-of-concept, weather sensor to detect rain droplet volume and wind velocity is developed. Although there are a lot of issues remained to move forward to realizing practical devices, proposed method may be able to open a door to build multi-modal and multi-tasking low-power sensor system in the future.

Semiconductor devices are inherently sensitive to radiation, exhibiting malfunctions called soft errors. Although soft error is sometimes misunderstood as an old and settled issue, it still makes marked negative impacts on our social and economic activities. Today’s testing of soft-error reliability relies heavily on exposure to radiation. Regardless of whether an effort aims for space or terrestrial applications, it is hard to know how reliable a chip is until tested with radiation. Since soft error is triggered by an energy deposition from radiation, the bombardment of radiation seems essential and unavoidable in test―Is it really true? The present study is attempting to answer this question. The authors are exploring equations capable of assessing soft error reliability proactively―as if by magic―without using radiation.

Plasma-material interaction (PMI) refers to phenomena that occur at the interface between plasma and solid surfaces. The plasma, which is high-energy and low-density, facilitates the formation of nanometer-scale structures on solid surfaces. Nanotechnology is supported by the PMI. In nuclear fusion research, plasma-induced microscopic changes in solid surfaces of the inner wall of the vacuum vessel are important issues. For instance, the reaction product helium plasma induces a fuzzy nanostructure on a tungsten surface.

　In the PMI, while focusing on spatially microscopic changes, the plasma irradiation time is extremely long, ranging from seconds to hours. Of course, some elementary processes occur on microscopic time scales, but in order to elucidate the whole, it is necessary to regard the PMI as a special subject in microscopic space and macroscopic time. From this point of view, I will introduce the difficulty and interest of the numerical simulation for the PMI, as well as future efforts.

Common issues when introducing statistical methods such as machine learning to measurements are explained, referring to the history of related technologies. The diversity of the physical and chemical context of materials and the universal applicability of measurements are taken up as assessment criteria, and the position of typical methods and their restrictions in applications are discussed. The importance of knowledge base and data linkage in the application of measurement informatics to materials development will then be addressed.