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DTSTAMP:20260524T085101Z
UID:https://www.fhi.mpg.de/events/43832/60031
DTSTART:20260108T130000Z
CLASS:PUBLIC
CREATED:20251114T081945Z
DESCRIPTION:Change notice: TH Department Seminar\nEstablishing a distribute
 d infrastructure for autonomous materials discovery and synthesis plays a 
 critical role in accelerating the development of advanced energy materials
  in areas like sustainable batteries and electrocatalysts for the green tr
 ansition. A central element in this process is the development of a closed
 -loop infrastructure or materials acceleration platform (MAP) [1\,2]\, whe
 re different nodes\, methods\, and even geographically distributed laborat
 ory equipment can work jointly using autonomous workflows [3] to co-optimi
 ze materials and device-level properties. Here\, we show an example using 
 the Fast INtention-Agnostic LEarning Server (FINALES) framework to orchest
 rate a two-pronged optimization task\, where both optimization tasks vary 
 the composition of a battery electrolyte composed of ethylene carbonate (E
 C)\, ethyl methyl carbonate (EMC)\, and lithium hexafluorophosphate (LiPF<
 sub>6</sub>). One targets the optimization of ionic conductivity\, while t
 he other aims to maximize the end-of-life (EOL) of coin cells [4].\nSpeake
 r: Prof. Tejs Vegge
LAST-MODIFIED:20251217T145649Z
LOCATION:https://zoom.us/j/98812731552?pwd=S8tLAYStkJi0PXba4DrMRRxcwGrbR1.1
 \, Room: Meeting ID: 988 1273 1552 | Passcode: 589437
ORGANIZER;CN=TH Department:mailto:
SUMMARY:AI-Orchestrated Computational Materials Discovery and Closed-Loop S
 ynthesis of Nanoparticles and Electrocatalysts 
URL;VALUE=URI:https://www.fhi.mpg.de/events/43832/60031
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