Chemistry Seminar with Dr. Shijing Sun from the University of Cambridge
Abstract: The possibilities for discovering new materials are boundless, yet physical resources are limited. To meet the growing energy demand, accelerated development of functional materials is more urgent than ever. In recent years, artificial intelligence (AI) and robotics have emerged as a transformative approach to accelerate scientific discovery. By coupling automated experiments with AI-driven decision-making, self-driving laboratories promise to reduce the materials innovation cycle from years to months. In this seminar, I will highlight three complementary approaches to building autonomous research platforms spanning organic synthesis, halide perovskites, and metal-organic frameworks. First, I will discuss the advantages and limitations of all-in-one liquid-handling systems for high-throughput exploration of synthesis conditions. Second, I will introduce low-cost, DIY robots built from open-source hardware aimed at democratizing access to laboratory automation and adapt to evolving research needs from ex situ to in situ measurements. Third, I will present modular thin-film deposition systems designed to empower scientific creativity through human-in-the-loop autonomy, exemplified by the optimization of solution-processed semiconductors. Together, these strategies demonstrate how automation and AI can augment human expertise, accelerating the synthesis and characterisation of diverse classes of advanced materials and opening new pathways toward next-generation energy technologies. I will conclude by discussing the open challenges of interfacing AI with real-world scientific experiments and the emerging opportunities the field presents, envisioning a future of seamless human-AI-robot collaboration for materials discovery.
About the speaker: Dr. Shijing Sun is an Associate Professor at the Department of Materials Science & Metallurgy, University of Cambridge. Her research focuses on developing self-driving laboratory platforms for energy materials. Dr. Sun read Natural Sciences at Trinity College, University of Cambridge, followed by a PhD under Prof. Anthony Cheetham in Cambridge and postdoctoral research at MIT with Prof. Tonio Buonassisi. She later became a Research Scientist at MIT, leading high-throughput synthesis and characterization for perovskite photovoltaics and optoelectronics. Before joining the University of Washington in Seattle, she worked at the Toyota Research Institute in Silicon Valley as a Senior Research Scientist, focusing on the application of AI to battery and fuel cell research. With over 70 publications and 50 invited talks worldwide, Dr Sun is also an affiliate faculty member at the Department of Mechanical Engineering, University of Washington, and was an Associate Editor for APL Machine Learning from 2023-2025.
Seminar hosted by Prof Zach Zheng