Materials Science and Engineering

We engage in research and development into the materials that will create breakthroughs in response to increasingly complex social demands, such as the environmental performance and economic rationality required by next-generation automobiles and energy systems. As part of our efforts to create energy storage and conversion materials, in addition to nano-interface reaction design and reaction control technologies, we also advance research and development into technologies that analyze and elucidate phenomena related to material structures and reactive processes at multiple scales using X-rays, light, and quantum beams. Moreover, we work with microfabrication technologies, manufacturing technologies including additive fabrication that integrates material processes and molding, material design at the atomic and molecular level based on computational science, and process development that enables the synthesis of these materials. In addition, we are actively engaged in materials informatics, autonomous and automated experiments, and other forms of fusion research with computer science as a means of dramatically improving the efficiency of materials development.

Core Technologies

Nanomaterials, Mathematical Physics and Matter Physics, Metal Materials Properties, Inorganic and Coordination Chemistry, Energy Chemistry, Chemical Reaction and Process System Engineering, Catalyst and Resource Chemical Process, Analytical Chemistry, Quantum Beam Science, Environmental Materials and Recycle Technology

Nano-interface Reaction Design and Reaction Control Technology

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.

Reproduced from J. Mater. Chem. A, 2020, 8, 25061(DOI: 10.1039/d0ta07707k) with permission from the Royal Society of Chemistry.

SEM images of a prototype nanostructure catalyst material
(The gaps formed between the particles achieve a larger surface area)

Data-driven Approach to Materials Design and Simulation

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.

Data-driven chemical calculation technology that learns on its own during simulations