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Abstract:

Self-organization and assembly processes are crucial steps in the formation of new phases and materials and can have a dramatic impact on their properties. For instance, the crystal structure, or polymorph, that forms during nucleation often dictates the mechanical and catalytic properties of metal nanoparticles, or the bioavailability of pharmaceutical drugs. Similarly, in biological and living systems, active particles can form intriguing patterns, swarms, or bacterial biofilms. While recent advances in nonequilibrium thermodynamics and statistical physics have started to shed light on the behavior of these systems, a complete understanding of these processes remains elusive. 

In this talk, I discuss how my 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. In this talk, I will discuss how particle-based simulations and artificial intelligence methods can be leveraged to shed light on assembly, cooperativity, and emergence. I will start by examining how entropy can be used as an order parameter, or collective variable, to unravel crystallization processes in interaction-controlled assembly processes. Then, I will examine how data science methods allow for the determination of entropy production and the in-depth analysis of the novel, motility-controlled, phase transitions exhibited by active matter and living systems.

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