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SUMMARY:High-throughput computational thermodynamics: a unique insight int
 o the potential energy surface - Livia Bartok-Partay\, University of Warwi
 ck
DTSTART:20240520T130000Z
DTEND:20240520T133000Z
UID:TALK216823@talks.cam.ac.uk
CONTACT:Eszter Varga-Umbrich
DESCRIPTION:During the past decade\, we have adapted the Bayesian statisti
 cal approach\, nested sampling\,\nfor exploring the potential energy surfa
 ce of atomistic systems.\nNested sampling's primary advantage lies in its 
 automatic sampling of\nthermodynamically relevant configurations across th
 e entire configuration space\,\nfrom gas phase to crystalline solid struct
 ures\, in proportion to their phase space volume.\nThis enables the calcul
 ation of the partition function at any temperature\,\nfacilitating the eva
 luation of key thermodynamic properties such as free energy\nand heat capa
 city. Nested sampling has proven particularly effective for sampling\nphas
 e transitions\, allowing us to calculate pressure-temperature phase diagra
 ms\nfor various materials and model systems. This unbiased and predictive 
 sampling\napproach has revealed surprising properties and new structures i
 n several systems\,\nenhancing our understanding of interatomic potential 
 models and guiding improvements\,\nincluding those in machine-learned pote
 ntials.\nThe talk describes the main features of the nested sampling metho
 d and highlights a\nfew such example applications.
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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