A digital laboratory with a modular measurement system and standardized data format

Abstract

Machine learning, robotics, and data are the keys for 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. In addition, the data are analyzed by software and utilized on the cloud. Using this system, we demonstrate an autonomous synthesis of high-quality LiCoO₂ (001) thin films. The system maximized the X-ray diffraction peak-intensity ratio of LiCoO₂ (001) thin films using Bayesian optimization. This system demonstrates advanced automatic and autonomous material synthesis for data- and robot-driven materials science.