Materials Science and Engineering

We are developing technologies that enable more precise analysis of materials and components in collaboration with cutting-edge quantum beam facilities such as SPring-8, a large synchrotron radiation facility, and J-PARC, a high-intensity proton accelerator facility. These technologies help us elucidate phenomena on an atomic scale and to measure the internal state and degradation of components.
At the Toyota Beamline (BL33XU) at the SPring-8, we use powerful X-rays to analyze the chemical state and crystal structure of materials. As an example, we successfully measured the three-dimensional distribution of internal stress in crystal grains constituting a material for the first time [Click here for details (Japanese)].
At the Material and Life Science Experimental Facility (MLF) at the J-PARC, we use neutron beams to analyze the state of water and organic materials inside components. As an example, using the energy-resolved neutron imaging system “RADEN”, we have successfully observed the behavior and freezing process of water inside a metal-covered fuel cell [Click here for movement details (Japanese), Click here for freezing process (Japanese)] .
These analytical technologies are expected to be applied not only to improve the performance and reliability evaluation of automotive components, but also to address social issues such as carbon neutrality and the circular economy.

Developing new energy storage and conversion materials as well as processes assembling the materials ranged over a wide length scale into a functional device requires advanced designing methods that combine computational physics, data science, and autonomous experimental systems together with empirical science methods. Conventional simulation methods, such as first-principles (FP) calculations, however, have severe limitations because of trade-off relationships between required computational time, accuracy and robustness. To solve the problem, we have devised an autonomous learning algorithm that generates interatomic interaction models of various materials automatically during FP simulations on the fly. The algorithm enables the machine to judge whether a structure appearing in the simulation is out of database or not. If the structure is judged to be new, the machine automatically gets FP data and reconstructs the interaction model. Otherwise, the structure is updated by using the interaction model. In this manner, most of FP calculations are bypassed, and two to four orders of magnitude faster simulations are realized. We will apply this new approach to calculations of ionic conductivity of electrolytes and activity of catalysts. Collaborations of the advanced simulations and experiments will pave the way to highly efficient materials design.

Controlling materials at the atomic level enables novel properties previously considered unattainable. As part of our research, we work on controlling the structure of abundant metals to impact functionalities comparable to those of precious metals.
For example, we have explored alternatives to cobalt and nickel species, both of which are commonly used as positive electrode materials in high-performance lithium-ion batteries. We focus on manganese species as sustainable alternatives and have demonstrated in small-sized batteries that lithium manganese oxides incorporating non-metallic elements such as boron or phosphorus into their crystal lattice can exhibit high capacity and extended cycling life.
This manganese-based approach is expected to enable high-performance batteries that are suitable for recycling.
This technology for controlling atomic-level structures holds promise for applications in a wide range of fields, from batteries to catalysts and energy generation.

In aims of creating high-efficiency energy conversion materials and applying them to adhesive bonding of different materials and substances, we are working to develop various key technologies, such as controlling reactions occurring at material interfaces and surfaces, designing material structures, and analyzing reaction fields. As an example, we focused on the high activity of nanoparticles and established a method that forms rutile IrO₂ crystal clusters with a diameter of 1 - 3 nm into free-standing, substrate-less fiber catalysts connected at the crystal domain boundaries by sputtering nanoparticles onto non-woven fabric’s surfaces made using electrospinning (NUNO: Nano particles United Non-woven-Object). Since the results have a large catalytic reaction surface due to the formation of gaps between the particles, these materials hold the potential for application to water-splitting catalysts with high activity, all while reducing the amount of material used.