I would like to discuss the impact of artificial intelligence and self-driving lab on the practice of research & development, in particular, clean energy research. ["Autonomous experiments using active learning and AI," Nature Reviews Materials 8 (2023) 563­564; "A multimodal robotic platform for multi-element electrocatalyst discovery," Nature 647 (2025) 390-396]   Rapid growth in modeling, experiment and reasoning capabilities, such as universal neural network interatomic potential (UNIP), large language model based hypothesis generation, robotic high-throughput experimentation, and knowledge-based Bayesian optimization, could usher in an era of “mass production of science”, but plenty of challenges and peril lie ahead.

Bio:
Ju Li has held faculty positions at the Ohio State University, the University of Pennsylvania, and is presently a chaired professor at MIT.  His group (http://Li.mit.edu) investigates the mechanical, electrochemical and transport behaviors of materials as  well as novel means of energy storage and conversion. Ju is a recipient of the 2005 Presidential Early Career Award for Scientists and Engineers, the 2006 Materials Research Society Outstanding Young Investigator Award, and the TR35 award fromTechnological Review. He was elected Fellow of the American Physical Society in 2014, a Fellow of the Materials Research Societyin 2017 and a Fellow of AAAS in 2020. Li is the chief organizer of MIT A+B Applied Energy Symposia that aim to develop solutions to global climate change challenges with “A-Action before 2040” and “B-Beyond 2040” technologies (www.youtube.com/@mitab3889">https://www.youtube.com/@mitab3889).