By Joanne Gagnon-Ketchen

Physics Colloquium, Wednesday March 22 at 4 p.m. in Olsen 102.

Prof. Jerome Delhommelle, Associate Professor of Chemistry at UML will give a talk on "Assembly, Cooperativity, and Emergence: From the AI-Guided Formation of Materials to the Onset of Soft Matter Robotics."

Self-organization and assembly processes are crucial steps in the making of a wide range of materials and, in turn, have a great impact on their performance. For instance, the crystal structure, or the polymorph, that forms during nucleation often dictates the bioavailability of pharmaceutical drugs or the catalytic properties of nanoparticles of metal alloys. In biology and medicine, protein folding, and aggregation play a major role in the onset of many neurodegenerative disorders. Similarly, active, self-propelled, objects can form unexpected structures such as living crystals, colloidal rotors, and bacterial biofilms. While recent experimental and computational advances have allowed for unprecedented insights into the behavior of nonequilibrium systems, a complete understanding of these processes has remained elusive so far. In this talk, I discuss how my research group leverages computational materials science and artificial intelligence to shed light on assembly, cooperativity, and emergence in hard, soft, and active matter. I show how AI-guided simulations shed light on assembly pathways in materials and biological systems, and how data science and machine learning provide a new way to accelerate discovery in soft autonomous robotics technology.

Jerome Delhommelle is a former student of the Ecole Normale Superieure of Paris-Saclay (France) and got his Ph.D. in Chemistry at the University of Paris-Saclay. Delhommelle has been awarded an NSF CAREER award from the Division of Materials Research and an Outstanding Young Faculty award from the Division of Computers in Chemistry of the American Chemical Society. His research group is currently funded by NSF (Division of Chemistry) and DOE (Materials Sciences and Engineering Division). Delhommelle recently co-authored an introductory textbook entitled “A Mole of Chemistry” (CRC Press) and he’s currently working on a graduate textbook: “Molecular Networking: Statistical Mechanics in the Age of AI and Machine Learning”. Delhommelle was recently elected to the Chair line of the Topical Group on Data Science (GDS) of the American Physical Society. He is also the North American editor for the journal “Molecular Simulation” (Taylor & Francis). In collaboration with Prof. Martin Karplus, 2013 Nobel Prize in Chemistry, he has developed a series of special issues on “Free Energy simulations” over the past 5 years.