Tokyo Institute of Technology · Engineering
Professor Ryota Shimizu's research lab specializes in advanced oxide materials and nanostructured functional films, with a focus on atomic-scale control of epitaxial oxide heterostructures, quantum phenomena in two-dimensional materials, and the development of autonomous materials discovery platforms. The lab combines cutting-edge techniques such as molecular beam epitaxy, scanning tunneling microscopy, and in situ spectroscopy to explore emergent electronic states like charge-density waves and superconductivity in complex oxides and graphene-based systems. A key innovation is the integration of machine learning and robotics for high-throughput, data-driven materials synthesis and optimization, enabling rapid exploration of complex materials spaces. The lab also investigates electric field distributions in dielectrics using advanced electro-optic imaging, supporting applications in energy and electronic devices.
Figures are computed from collected data and may differ slightly.
Future materials-science research will involve autonomous synthesis and characterization, requiring an approach that combines machine learning, robotics, and big data. In this paper, we highlight our recent experiments in autonomous synthesis and resistance minimization of Nb-doped TiO2 thin films. Combining Bayesian optimization with robotics, these experiments illustrate how the required speed and volume of future big-data collection in materials science will be achieved and demonstrate the tr
Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ryota Shimizu, Katsuya Iwaya, Takeo Ohsawa, Susumu Shiraki, Tetsuya Hasegawa, Tomihiro Hashizume, Taro Hitosugi; Effect of oxygen deficiency on SrTiO3(001) surface reconstructions. Appl. Phys. Lett. 25 June 2012; 100 (26): 263106. https://doi.org/10.1063/1.4730409 Download cita
The initial homoepitaxial growth of SrTiO(3) on a (√13 × √13)-R33.7° SrTiO(3)(001) substrate surface, which can be prepared under oxide growth conditions, is atomically resolved by scanning tunneling microscopy. The identical (√13 × √13) atomic structure is clearly visualized on the deposited SrTiO(3) film surface as well as on the substrate. This result indicates the transfer of the topmost Ti-rich (√13 × √13) structure to the film surface and atomic-scale coherent epitaxy at the film/substrate
We report the emergence of a charge-density wave (CDW) in Ca-intercalated bilayer graphene (C_{6}CaC_{6}), the thinnest limit of superconducting C_{6}Ca, observed by low-temperature, high-magnetic-field scanning tunneling microscopy or spectroscopy, and angle-resolved photoemission spectroscopy. While the possible superconductivity was not observed in epitaxially grown C_{6}CaC_{6} on a SiC substrate, a CDW order different from that observed on the surface of bulk C_{6}Ca was observed. It is inf
We propose a novel method of training neural networks for industrial image classification that can reduce the effect of imbalanced data in supervised training. We considered visual quality inspection of industrial products as an image-classification task and attempted to solve this with a convolutional neural network; however, a problem of imbalanced data emerged in supervised training in which the neural network cannot optimize parameters. Since most industrial products are not defective, sampl
We have developed a method to measure 3-dimensional electric field distributions in liquid dielectrics using the Kerr electro-optic effect combined with ac field modulation and circularly polarized light. We derived theoretical relations between an arbitrary electric field vector and a transmitted light intensity. The derived equations were verified by comparing the theoretical electric field profile with the experimental one in transformer oil with an axisymmetric electrode configuration, i.e.
Personal and easy-to-use health checking system is an attractive application of sensor systems. Sensing data analysis for diagnosis is important as well as preparing small and mobile sensor nodes because sensing data include variations and noises reflecting individual difference of people and sensing conditions. Deep Neural Network, or Deep Learning, is a well-known method of machine learning and it is effective for feature extraction from pictures. Then, we thought Deep Learning also can extrac
Currently, no known material retains its photoinduced metallic conductivity over a long time while also exhibiting repeatable metal-to-insulator recovery. Here, we demonstrate such a highly repeatable photoinduced insulator-to-metal transition in yttrium oxyhydride (YOxHy) epitaxial thin films. The temperature (T) dependence of the electrical resistivity (ρ) of the films transforms from the insulating to the metallic state (dρ/dT > 0) under ultraviolet laser illumination. The YOxHy film recovers
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