Abstract: Innovation in energy materials and devices is essential for addressing global challenges such as climate change. Artificial intelligence (AI) has emerged as a powerful tool to accelerate materials discovery, but there are still challenges in realizing the potential of computational designs in the laboratory. One question often gets asked on self-driving labs is that ​‘will robots replace scientists?’

In this seminar, I will discuss, rather than replacing researchers, how emerging technologies can augment and amplify human expertise, leading to unprecedented breakthroughs in energy storage and conversion. As an experimentalist and materials data scientist with experience in both academia and industry, I will present examples of data-driven approaches that can address atomic-to-device level materials science challenges. From high-throughput battery device testing to closed-loop inorganic materials design and open-ended exploratory synthesis, I will focus on how to predict experimental outcomes, explain results with interpretable machine learning, and design new experiments that incorporate physical knowledge into an automated framework, thereby guiding the discovery of new materials and the design of new systems.

Bio: Shijing Sun is a senior research scientist at the Energy & Materials Division, Toyota Research Institute (TRI). Prior to joining TRI, Dr. Sun was a research scientist of mechanical engineering at MIT. Sun obtained B.A., M.Sci. and Ph.D. degrees in materials science at Trinity College, University of Cambridge.