Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. 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 the power of 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.

Prepare reaction material Monitor turbidity using computer vision Process solubility data and prepare for next experiment Reproducible data within range of manual methodsDose Liquid 2 Dose Solid 1 Record Image 4 Measure Turbidity 5 Determine if solution is stable and dissolved

Chemists spend an inordinate amount of time performing low-level tasks based on visual observation. Camera-enabled laboratory equipment in conversation with computer vision algorithms can be used to automate many of these processes, thereby freeing up valuable time and resources. We developed a generalizable computer-vision based system capable of monitoring and controlling liquid-level across a variety of chemistry applications. This paper reports on the system’s motivation, architecture, and successful deployment in three experimental use cases which require continous stirring: continuous preferential crystallization (CPC), slurry filtration, and solvent swap distillation

Teaching laboratories are designed to educate students on basic theories and techniques through guided laboratory experiments. As advances in automation revolutionize research chemistry laboratories, teaching tools can advance to reflect these changes. In this study, we integrated several programmable instruments, including automated pumps for liquid transfer, into an augmented titration setup that can be operated remotely. Our setup empowers students to learn the conceptual, technical, and practical aspects of titration by leveraging laboratory automation tools. Our system uses Siri as a speech recognition tool and the Python programming language to develop action routines for titration uses. The application of Siri provides a smooth user-toinstrument experience, and the use of Python allows coders to effectively develop and maintain the workflow. Three chemistry professors were invited to participate in usability tests of the system using a mobile device. Their positive feedback highlights the promise of our system as a valuable addition to undergraduate chemistry teaching laboratories. This titration setup addresses accessibility challenges and distance learning requirements for chemical education. Further, our tool introduces students to the type of automated experimentation that is emerging as the modern standard in chemistry research laboratories.

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