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VERSION:2.0
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BEGIN:VEVENT
DTSTAMP:20261016T025019Z
UID:https://www.mpip-mainz.mpg.de/events/27812/536464
DTSTART:20210330T130000Z
DTEND:20210330T140000Z
CLASS:PUBLIC
CREATED:20210330T083337Z
DESCRIPTION:Self-organization and assembly processes are crucial steps in t
 he 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 phar
 maceutical drugs\, or the mechanical and catalytic properties of metal all
 oys and inorganic nanoparticles. In biology and medicine\, protein folding
  and aggregation processes play a major role in the onset of many neurodeg
 enerative disorders. Similarly\, active\, self-propelled\, objects can for
 m unexpected structures such as colloidal rotors on the micron scale\, or 
 bacterial biofilms\, bird flocks and swarms of unmanned aerial systems on 
 the macroscopic scale. While recent advances in experimental\, theoretical
  &amp\; computational methods have allowed for unprecedented insights into
  the behavior of nonequilibrium systems\, a complete understanding of thes
 e processes has remained elusive so far. For example\, it is still impossi
 ble to predict which crystal structure forms when a liquid crystallizes. S
 imilarly\, the elucidation of the rules of life of swarms and active assem
 blies remains an outstanding challenge\, although it is a necessary starti
 ng point to the successful development of soft matter robotics. In this ta
 lk\, I discuss how my research group leverages computational materials sci
 ence and artificial intelligence to shed light on assembly\, cooperativity
 \, and emergence in hard\, soft and active matter. I show how recent advan
 ces in statistical mechanics and ML-guided simulations shed light on assem
 bly pathways in materials and biological systems. I finally highlight how 
 data science and machine learning methods provide a new way to accelerate 
 discovery in soft autonomous robotics technology.\nVortragende(r): Prof. J
 erome Delhommelle
LAST-MODIFIED:20210330T083835Z
LOCATION:Digital
ORGANIZER;CN=Robinson Cortes Huerto:mailto:corteshu@mpip-mainz.mpg.de
SUMMARY:Assembly\, Cooperativity\, and Emergence: From the AI-Guided Format
 ion of Materials to the Onset of Soft Matter Robotics
URL;VALUE=URI:https://www.mpip-mainz.mpg.de/events/27812/536464
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