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SUMMARY:Phonons for high-throughput phase diagram prediction from ephemera
 l data-derived potentials - Pascal Salzbrenner\, University of Cambridge
DTSTART:20240513T133000Z
DTEND:20240513T140000Z
UID:TALK216526@talks.cam.ac.uk
CONTACT:Dr Sun-Woo Kim
DESCRIPTION:Machine-learned interatomic potentials open up the possibility
  of high-throughput pressure-temperature phase diagram prediction [1]. Pho
 nons are a key component of these calculations. The use of interatomic pot
 entials in lattice dynamics simulations has a long history\, as have their
  inaccuracies resulting from the lack of electronic structure information.
  While there is a rich and growing literature on the calculation of phonon
 s with machine-learned potentials [2]\, it is therefore not obvious a prio
 ri that a general-purpose potential will have the accuracy across structur
 es and pressures necessary to predict high-quality phase diagrams.\nIn thi
 s talk\, I show how to use the ephemeral data-derived potentials (EDDPs) [
 3] in high-throughput phonon calculations. I discuss their strengths as we
 ll as weaknesses\, and demonstrate how to balance them in order to success
 fully predict the phase diagram of elemental lead up to 20 GPa and 2500 K.
 \n\n[1] Pascal T. Salzbrenner\, Se Hun Joo\, Lewis J. Conway\, Peter I. C.
  Cooke\, Bonan Zhu\, Milosz P. Matraszek\, William C. Witt\, Chris J. Pick
 ard\; Developments and further applications of ephemeral data derived pote
 ntials. J. Chem. Phys. October 2023\; 159 (14): 144801.\n[2] Janine George
 \, Geoffroy Hautier\, Albert P. Bartók\, Gábor Csányi\, Volker L. Derin
 ger\; Combining phonon accuracy with high transferability in Gaussian appr
 oximation potential models. J. Chem. Phys. July 2020\; 153 (4): 044104.\n[
 3] Chris J. Pickard\; Ephemeral data derived potentials for random structu
 re search. Phys. Rev. B. July 2022\; 106: 014102
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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