Taro Hitosugi
Department of Chemistry, The University of Tokyo
Tokyo, Japan
Monday, 23rd March 2026, 17:00 s.t.
The talk will be given in hybrid mode.
You can join at:
Hörsaal 2
Faculty of Chemistry, University of Vienna
Währinger Straße 42, 1090 Vienna
You can also join the Zoom meeting:
https://tuwien.zoom.us/j/92739417554?pwd=MlFkNjJxUjFkUUhPaUJmZ0ZnMjVOZz09
Meeting ID: 927 3941 7554, Passcode: X74b82XE
The talk will also be streamed via u:stream:
https://ustream.univie.ac.at/live/53ee2769-8419-4f81-8cac-d29c1b07050a
Monday, 23rd March 2026, 17:00 s.t.
The talk will be given in hybrid mode.
You can join at:
Freihaus Hörsaal 4 (HS 4)
TU Freihaus, Yellow Area, 2nd floor
Wiedner Hauptstraße 8, 1040 Vienna
Or you can join the zoom meeting:
https://tuwien.zoom.us/j/92739417554?pwd=MlFkNjJxUjFkUUhPaUJmZ0ZnMjVOZz09
Meeting ID: 927 3941 7554 Passcode: X74b82XE
Autonomous Experiments for Solid Materials: From Thin Films to Bulk Synthesis
Autonomous experiments that integrate machine learning and robotics are reshaping materials research. By automating experimental workflows and efficiently searching high-dimensional parameter spaces, these approaches markedly accelerate materials discovery and process optimization.
Here, we report a modular self-driving laboratory (SDL) for solids and thin films [1–4]. The SDL orchestrates all stages of the experimental cycle—including sample transfer, synthesis, characterization, and iterative optimization. Data acquisition spans X-ray diffraction, scanning electron microscopy, Raman spectroscopy, and optical transmittance measurements. A Bayesian optimization enables autonomous exploration of the parameter space and rapid identification of optimal conditions.
We demonstrate the platform by synthesizing thin films of TiO₂ and LiCoO2. We further show that the same workflow supports the discovery of new ionic conductors. These results highlight the potential of autonomous experimentation to accelerate research in solid-state materials. Ongoing efforts extend the SDL to bulk-materials synthesis, aiming to unify thin-film and bulk workflows within a single autonomous framework.
[1] “Autonomous experimental systems in materials science” N. Ishizuki, R. Shimizu, and T. Hitosugi, STAM Methods 3, 2197519 (2023).
[2] “Autonomous materials synthesis by machine learning and robotics” R. Shimizu, T. Hitosugi et al., APL Mater. 8111110 (2020).
[3] “Autonomous exploration of an unexpected electrode material for lithium batteries” S. Kobayashi, T. Hitosugi et al., ACS Materials Lett. 5, 2711–2717 (2023).
[4] “Digital laboratory with modular measurement system and standardized data format” K. Nishio, T. Hitosugi et al., Digital Discovery 4, 1734-1742 (2025).
Bio of Taro Hitosugi
Taro Hitosugi is a Professor of Chemistry at The University of Tokyo. He received his Ph.D. from The University of Tokyo in 1999 and began his career at Sony Corporation. In 2003, he returned to academia as an Assistant Professor at The University of Tokyo. He was an Associate Professor at Tohoku University before becoming a full professor at the Tokyo Institute of Technology (Institute of Science Tokyo) in 2015. He was appointed as a full professor at The University of Tokyo in 2022.
As an expert in solid-state chemistry, he focuses on materials for electronics and energy applications. His work includes the synthesis and characterization of thin-film materials and the surfaces and interfaces. In addition, he developed an autonomous materials synthesis system that uses machine learning and robotics, to accelerate materials science research. He has authored more than 220 peer-reviewed publications in leading academic journals. Professor Hitosugi contributes his expertise to the Cabinet Office’s “Materials Strategy” and the Science Council of Japan.