Automated chemical synthesis and measurement devices have begun to be recognized as indispensable tools for improving efficiency and speed in experiments, enabling control and recording of experimental systems with high precision, reproducibility, and efficiency . The research of automated experiments have a history of several decades, but recent advances in information science have ushered in a new paradigm . Typical achievements include the use of image recognition systems for assessing experimental conditions , prediction of experimental results through models that learn structures and property correlations from large datasets , and suggesting experimental conditions using search algorithms like Bayesian optimization and sparse modeling .