November 7, 2022
Email office@phys.ksu.edu for the Zoom address
Self-organization and assembly processes are crucial steps in the formation of new phases and materials, and can have, as a result, 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 physics, active, or self-propelled, particles can form intriguing patterns and swarms. While recent advances have been made in nonequilibrium statistical physics, a complete understanding of these processes remains elusive.
In this talk, I discuss how computational physics and artificial intelligence methods can be leveraged to shed light on assembly, cooperativity, and emergence. I start by examining how entropy can be used as an order parameter, or collective variable, to unravel phase transitions in bulk and nanoconfined systems. Then, I show how data science methods can be utilized to determine entropy production and allow for the in-depth analysis of the novel, motility-induced, phase transitions exhibited by active matter.
