I work at the exciting intersection of machine learning and materials science. I am driven by the potential of cutting-edge machine learning techniques to accelerate materials discovery and deepen our understanding of material properties, ultimately contributing to a more sustainable future.
My background in materials science, specializing in the crystal structure analysis of functional inorganic materials, provides me with a solid foundation to explore this promising field.
The vast amounts of data generated in materials development often pose significant analytical challenges, creating a bottleneck in research and leading to the underutilization of valuable information. My research focuses on developing machine learning algorithms to streamline this process, particularly for complex data like X-ray diffraction, uncovering hidden patterns and accelerating discoveries.
For decades, materials discovery has been performed by Edisonian trial-and-error approach - like searching for a specific object in a dark room. My research leverages machine learning and computational methods to illuminate the vast universe of possible materials, accelerating the discovery of innovative materials with desired properties.
My recent publications are listed below. For a complete list, please visit my Google Scholar profile.
Machine learning for materials science.
School of High Energy Accelerator Science
Department of Materials Structure Science
KEK Ono Laboratory http://ono.kek.jp
Faculty of Industrial Science and Technology
Department of Materials Science and Technology
Kotsugi Laboratory https://www.kotsugi.jp
Institute of Materials Structure Science
Ono Laboratory http://ono.kek.jp
Development of a novel catalyst.
Faculty of Industrial Science and Technology
Department of Materials Science and Technology
resnant(at)outlook.jp