Key Takeaways Researchers developed AutoBot, an automated, AI-driven laboratory that accelerates optimization of materials synthesis. In just a few weeks, AutoBot pinpointed the best combinations of synthesis parameters for materials called metal halide perovskites. This process would have taken up to a year with traditional, manual experimentation. AutoBot’s iterative learning approach can be expanded to enable cost-effective, industrial-scale manufacturing for a wide range of optical materials and devices. A research team led by the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) has built and successfully demonstrated an automated experimentation platform to optimize the fabrication of advanced materials. The platform, called AutoBot, uses machine learning algorithms to direct robotic devices to rapidly synthesize and characterize materials. The algorithms automatically refine the experiments based on analysis of the characterization results. The researchers tested the platform on an emerging class of materials called metal halide perovskites that show promise for applications such as light-emitting diodes (LEDs), lasers, and photodetectors. It took AutoBot just a few weeks to explore numerous combinations of fabrication parameters to find the combinations that yield the highest quality materials. Informed by machine learning algorithms with a super-fast learning rate, AutoBot needed to experimentally sample just 1% of the 5,000 combinations to find this ‘sweet spot.’ This process would have taken up to a year with the traditional trial-and-error approach, where researchers manually test one set of parameters at a time, guided by previous experience and intuition. “AutoBot represents a paradigm shift for material exploration and optimization” – Carolin Sutter-Fella “AutoBot represents a paradigm shift for material exploration and optimization,” said Carolin Sutter-Fella, a Berkeley Lab scientist and one of the study’s corresponding authors. “By integrating synthesis, characterization, robotics, and machine learning capabilities in a single platform, AutoBot dramatically accelerates the process of screening synthesis recipes. Its rapid learning approach is a significant step toward establishing autonomous optimization laboratories and can be expanded to a wide range of materials and devices.” Scientists at the Molecular Foundry—a Department of Energy Office of Science User Facility located at Berkeley Lab—conceived the idea for AutoBot, expanded on a commercial robotics platform, and implemented solutions for data processing, analysis, and machine learning infrastructure. The multidisciplinary team included researchers from the University of Washington, University of Nevada, University of California-Davis, University of California-Berkeley, and Friedrich-Alexander-Universität Erlangen-Nürnberg. The scientists report their work in the journal Advanced Energy Materials.