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- Ryota Shimizu
- School of Materials and Chemical Technology, Tokyo Institute of Technology 1 , Meguro, Tokyo 152-8552, Japan
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- Shigeru Kobayashi
- School of Materials and Chemical Technology, Tokyo Institute of Technology 1 , Meguro, Tokyo 152-8552, Japan
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- Yuki Watanabe
- School of Materials and Chemical Technology, Tokyo Institute of Technology 1 , Meguro, Tokyo 152-8552, Japan
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- Yasunobu Ando
- Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology 3 , Tsukuba, Ibaraki 305-8568, Japan
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- Taro Hitosugi
- School of Materials and Chemical Technology, Tokyo Institute of Technology 1 , Meguro, Tokyo 152-8552, Japan
説明
<jats:p>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 tremendous potential of this combined approach. We briefly discuss the outlook and significance of these results and advances.</jats:p>
収録刊行物
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- APL Materials
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APL Materials 8 (11), 111110-, 2020-11-01
AIP Publishing
