| Abstract Scope |
Machine learning, robotics, and data are the keys to accelerating the discovery of new materials. While collecting more data is essential, the experimental processes remain a bottleneck. In this study, we constructed a digital laboratory by interconnecting apparatuses using robots to collect experimental data (synthesis processes and measured physical properties, including measurement conditions) for solid materials research. A variety of modular experimental instruments are physically interconnected, enabling fully automated processes from material synthesis to measurement and analysis. The data from each measurement instrument are outputted in an XML format, namely MaiML, and collected in a cloud-based database. The data is analyzed and utilized on the cloud. Using this system, we demonstrate an autonomous synthesis of high-quality LiCoO2 thin films. The system maximized the X-ray diffraction peak of LiCoO2 thin films using Bayesian optimization. This system demonstrates advanced autonomous material synthesis for data- and robot-driven materials science. |