  Discovering and optimizing commercially viable materials for clean energy
applications typically takes over a decade. Self-driving laboratories that
iteratively design, execute, and learn from material science experiments in a
fully autonomous loop present an opportunity to accelerate this research. We
report here a modular robotic platform driven by a model-based optimization
algorithm capable of autonomously optimizing the optical and electronic
properties of thin-film materials by modifying the film composition and
processing conditions. We demonstrate this platform by using it to maximize the
hole mobility of organic hole transport materials commonly used in perovskite
solar cells and consumer electronics. This demonstration highlights the
possibilities of using autonomous laboratories to discover organic and
inorganic materials relevant to materials sciences and clean energy
technologies.
