Chemistry Departmental Seminar

About the event

Departmental Seminar

Speaker: Dr. Jerome Delhommelle
Associate Professor, Department of Chemistry
Associate Professor, SEECS (Biomedical Engineering)
Adjunct, Physics & Astrophysics

University of North Dakota

https://und.edu/directory/jerome.delhommelle

Host: Professor Kirk Peterson

Title: Assembly, Cooperativity, and Emergence: From the AI-Guided Formation of Materials to the Onset of Soft Matter Robotics

Abstract: 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 polymorph, that forms during nucleation often dictates the bioavailability of pharmaceutical drugs, or the mechanical and catalytic properties of metal alloys and inorganic nanoparticles. In biology and medicine, protein folding and aggregation processes play a major role in the onset of many neurodegenerative disorders. Similarly, active, self-propelled, objects can form unexpected structures such as colloidal rotors on the micron scale, or bacterial biofilms, flocks of birds and swarms of unmanned aerial systems on the macroscopic scale. While recent advances in experimental, theoretical & computational methods have allowed for unprecedented insights into the behavior of nonequilibrium systems, a complete understanding of these processes hasremained elusive so far. For example, it is still impossible to predict which crystal structure forms when a liquid crystallizes. Similarly, the elucidation of the rules of life of swarms and active assemblies remains an outstanding challenge, although it is a necessary starting point to the successful development of soft matter robotics. 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.