Team develops digital lab for data- and robot-driven materials science
Researchers at the University of Tokyo and their collaborators have developed a digital laboratory system that fully automates the material synthesis and the structural and physical property evaluation of thin-film samples.
With the digital laboratory, or dLab, the team can autonomously synthesize thin-film samples and measure their material properties. The system demonstrates advanced automatic and autonomous material synthesis for data- and robot-driven materials science.
The research is published in the journal Digital Discovery.
Machine learning, robotics and data are deemed vital to the discovery of new materials. However, although data collection is an essential component, there is a bottleneck in that part of the experimental process.
So, researchers constructed a digital laboratory with interconnected apparatuses for solid materials research. They used robots to collect experimental data, such as synthesis processes, and measured physical properties, including measurement conditions.
Their dLab consists of a variety of modular experimental instruments that are physically interconnected. This allows researchers to fully automate processes from material synthesis to a wide range of measurements for surface microstructures, X-ray diffraction patterns, Raman spectra (a chemical analysis technique using scattered light), electrical conductivity and optical transmittance.
The diagram shows the overall modular synthesis system (sputtering deposition system), which is connected to various measurement and analysis devices. Details of the shape of the sample holder and the communication protocols used when connecting each module have been made publicly available by the research team. Credit: Kazunori Nishio, Science Tokyo
The automated analysis program operates by smoothing data, then detecting, classifying and indexing peaks. Credit: Kei Takihara, Akira Aiba, Kazunori Nishio, Science Tokyo
The system searches for optimal temperature autonomously, using Bayesian optimization. Credit: Kei Takihara, Akira Aiba, Kazunori Nishio, Science Tokyo
The system consists of a collection of microservices, with the layers below the orchestration software forming the physical layer directly connected to the hardware. Credit: Taro Hitosugi, Akira Aiba, The University of Tokyo, Science Tokyo