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.
Highlights
Materials are a powerhouse of innovation,1 and accelerating the development of new materials is vital for a sustainable society
With the ongoing evolution of machine learning and robotics, the CASH movement is steadily spreading around the world and is expected to have a transformative effect on basic research
The closed-loop cycles for minimizing the T2 thin film resistance are illustrated by Figs. 3(d)–3(f)
Summary
Materials are a powerhouse of innovation, and accelerating the development of new materials is vital for a sustainable society. New materials development in laboratories involves repeated cycles of conception, synthesis, and characterization, manually performed by researchers. The inclusion of machine learning, robotics, and big data into these cycles promises to revolutionize materials research and beyond. Let us sketch the concept of a next-generation materials research laboratory. Future lab equipment and instruments should be Connected, Autonomous, Shared, and operate in a Highthroughput manner (CASH, Fig. 1). With the ongoing evolution of machine learning and robotics, the CASH movement is steadily spreading around the world and is expected to have a transformative effect on basic research
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