Autonomous materials synthesis by machine learning and robotics

DOI PDF 29 Citations Open Access
  • Ryota Shimizu
    School of Materials and Chemical Technology, Tokyo Institute of Technology 1 , Meguro, Tokyo 152-8552, Japan
  • Shigeru Kobayashi
    School of Materials and Chemical Technology, Tokyo Institute of Technology 1 , Meguro, Tokyo 152-8552, Japan
  • Yuki Watanabe
    School of Materials and Chemical Technology, Tokyo Institute of Technology 1 , Meguro, Tokyo 152-8552, Japan
  • 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
  • Taro Hitosugi
    School of Materials and Chemical Technology, Tokyo Institute of Technology 1 , Meguro, Tokyo 152-8552, Japan

Description

<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>

Journal

  • APL Materials

    APL Materials 8 (11), 111110-, 2020-11-01

    AIP Publishing

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