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SUMMARY:Neural network sampling of the free energy landscapes for quantum 
 defect formation in silicon carbide - Prof Elizabeth M. Y. Lee\, Universit
 y of California\, Irvine
DTSTART:20221121T143000Z
DTEND:20221121T150000Z
UID:TALK192230@talks.cam.ac.uk
CONTACT:Dr M. Simoncelli
DESCRIPTION:Engineering next-generation electronic devices\, ranging from 
 battery electrodes\, photoelectrocatalysts\, to solid-state quantum sensor
 s\, requires precise knowledge of how the structural arrangement of atoms 
 impact the electronic properties of materials during synthesis and device 
 operation. A promising tool for studying this is first-principles molecula
 r dynamics (FPMD) based on density functional theory. However\, these simu
 lations are computationally expensive\, which hinders their application. I
 n this talk\, I will discuss a computational modeling framework that combi
 nes FPMD with enhanced sampling and machine learning\, to reveal electron 
 spins and molecular reactions\, as materials undergo electronic and struct
 ural changes. The first part will focus on the development of a neural net
 work approach in simulating reactions in condensed phase systems. These al
 gorithms enable the simulation of reactive processes\, for instance\, in t
 he molecular nitrogen dissociation on metal catalysts\, which is the rate-
 determining step in ammonia synthesis [1]. In the second part\, I will dis
 cuss modeling the high-temperature formation of quantum defects in solids 
 to realize scalable quantum systems. This study reveals that understanding
  electronic structure and dynamics of spin defects are keys to designing n
 ew quantum technologies [2].\n \n[1] E. M.Y. Lee\, T. Ludwig\, B. Yu\, A. 
 Singh\, F. Gygi\, J. K. Norskov\, J. J. de Pablo. J. Phys. Chem. Letters 1
 2\, 2954-2962 (2021)\n\n[2] E. M.Y. Lee\, A. Yu\, J. J. de Pablo\, and G. 
 Galli. Nature Communications\, 12\, 6325\, 2021
LOCATION:Zoom
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