Modeling Atoms to Address Our Climate Crisis
EECS Colloquium
Wednesday, October 4, 2023
306 Soda Hall (HP Auditorium)
4:00 – 5:00 pm
Larry Zitnick, Meta-FAIR
Research Director
Abstract
Climate change is a societal and political problem whose impact could be mitigated by technology. Underlying many of its technical challenges is a surprisingly simple yet challenging problem; modeling the interaction of atoms. In this talk, we motivate the problem and provide insights into how this opens up new intriguing directions for machine learning and AI researchers. Recent large-scale datasets released by the Open Catalyst Project enable the training of ML models that generalize across a broad range of the chemical space. Analogies are drawn to computer vision to map recent state-of-the-art approaches for atomic modeling to a more familiar domain. We conclude by exploring the numerous open problems and their potential for wide-ranging impact beyond climate change.
Biography
Larry Zitnick is a research director on the Fundamental AI Research team at Meta. He is currently focused on scientific applications of AI and machine learning, such as the discovery of new catalysts for renewable energy applications. Previously, his research in computer vision covered many areas such as the FastMRI project to speed up the acquisition of MRIs, and the COCO and VQA datasets to benchmark object detection and visual language tasks. He developed the PhotoDNA technology used by industry and various law enforcement agencies to combat illegal imagery on the web. Before joining FAIR, he was a principal researcher at Microsoft Research. He received a Ph.D. degree in robotics from Carnegie Mellon University.