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SUMMARY:Thermodynamic properties by on-the-fly machine-learned potentials 
 within and beyond DFT - Carla Verdi\, University of Vienna
DTSTART:20220328T130000Z
DTEND:20220328T133000Z
UID:TALK167312@talks.cam.ac.uk
CONTACT:Dr M. Simoncelli
DESCRIPTION:Machine-learned interatomic potentials enable realistic finite
  temperature calculations of complex materials properties with first-princ
 iples accuracy. It is not yet clear\, however\, how accurately they descri
 be anharmonic properties\, which are crucial for predicting the lattice th
 ermal conductivity and phase transitions in solids and\, thus\, shape thei
 r technological applications. In this talk I will discuss a recently devel
 oped on-the-fly learning technique based on molecular dynamics and Bayesia
 n inference\, and I will show how it can be employed in order to generate 
 accurate force fields that are capable to predict thermodynamic properties
 . For the paradigmatic example of zirconia\, an important transition metal
  oxide\, I will show that our machine-learned potential correctly captures
  the temperature-induced phase transitions below the melting point. It can
  also be used to calculate the heat transport on the basis of Green-Kubo t
 heory\, accounting for anharmonic effects to all orders. In addition\, I w
 ill introduce a ∆-machine learning approach that allows to train interat
 omic potentials from beyond-density functional theory calculations at an a
 ffordable computational cost. The results demonstrate that these technique
 s enable many-body calculations of finite-temperature properties of materi
 als.
LOCATION:Venue to be confirmed
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