Artificial intelligence (AI) and robotics have emerged as transformative tools to accelerate materials research, however, challenges remain in realizing the full potential of computational designs in laboratory settings. With the rise of self-driving laboratories powered by automated experiments and AI-driven guidance, a paradigm shift in experimental materials science is underway. One question increasingly asked in the context of building autonomous laboratories is, ‘will robots replace scientists?’ In this talk, we will explore how emerging technologies can augment and amplify human expertise, introducing the concept of collaborative intelligence in shaping the labs of the future.
One of the key areas of application for self-driving labs is in energy and sustainability, where materials design and discovery are foundational to developing renewable energy technologies, energy storage systems, and energy-efficient devices, all of which play critical roles in mitigating climate change. In this talk, we will outline three pathways for assembling self-driving labs to address the varying needs of materials discovery, involving the handling both organic and inorganic building blocks in the forms of solutions, crystals and thin films. We will discuss all-in- one, fully autonomous platforms that aimed for high exploration speed with robustness and standardization; modular integrations leveraging low-fidelity proxies as metrics for experimental outcomes while maintaining effective orchestration; and low-cost, open-source hardware that democratizes science by enabling researchers to design customized robotic systems tailored to the evolving research needs. By moving away from labor-intensive bench chemistry to collaborative systems, we are transforming how advanced energy materials are designed, synthesized, and optimized. Together, these strategies define a new era of experimental materials science, offering unprecedented opportunities for innovation and advancing sustainable technologies.
Dr. Shijing Sun is an assistant professor at the University of Washington. She completed her academic studies at Trinity College, University of Cambridge. At Cambridge, she obtained her B.A. in Natural Sciences, as well as M.Sci. and Ph.D. degrees in materials science.
Dr. Sun joined UW from Toyota Research Institute (TRI) in Silicon Valley, where she held the position of senior research scientist. During her time at TRI, she focused on developing AI-powered solutions aimed at accelerating the development of electric vehicle (EV) batteries and fuel cells for carbon-neutral mobility. Previously, Dr. Sun worked as a research scientist at the Department of Mechanical Engineering at MIT. In this role, she led a team that specialized in developing high-throughput synthesis and characterization methods for thin-film solar cells.
Dr. Sun is passionate about materials informatics education. She served as an instructor for data science tutorials at the Materials Research Societies Fall (2021, 2022) and Spring (2023) Meetings. She also currently serves as an associate editor for APL Machine Learning.
(Link to Biography) UW MechEng
(Link to Biography) UW Sun Lab
