Autonomous materials synthesis by machine learning and robotics

Abstract

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.