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UID:pretalx-juliacon2023-GGEKXE@pretalx.com
DTSTART;TZID=EST:20230728T161000
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DESCRIPTION:Scaling up atomistic simulation models is hampered by expensive
  calculations of interatomic forces. Machine learning potentials address t
 his challenge and promise the accuracy of first-principles methods at a lo
 wer computational cost. This talk presents\, as part of the research activ
 ities of the CESMIX project\, how Julia is used to facilitate automating t
 he composition of a novel neural potential based on the Atomic Cluster Exp
 ansion.
DTSTAMP:20260415T094430Z
LOCATION:26-100
SUMMARY:Automating the composition of ML interatomic potentials in Julia - 
 Emmanuel Lujan
URL:https://pretalx.com/juliacon2023/talk/GGEKXE/
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