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BEGIN:VEVENT
DTSTAMP:20260612T080933Z
DTSTART;VALUE=DATE-TIME:20251117T130000
DTEND;VALUE=DATE-TIME:20251117T140000
SUMMARY:WCPM: Simone Swantje Köcher\, Research Centre Jülich
TZID:Europe/London
UID:20251117-8ac672c498efcc010198f086fda80452@warwick.ac.uk
CREATED:20250908T114213Z
DESCRIPTION:Location: Lecture Theatre 0.04 IMC Networking Lunch: The Rech
 arge Room\, next to Lecture Theatre 004\, from 12:30pm - 1pm. Title: Syn
 ergy of Theory and Experimental NMR in Energy Material Research Abstract
 : Nuclear magnetic resonance (NMR) spectroscopy provides a powerful tool
  for probing high-performance energy materials such as solid ion conduct
 ors. Probing different spin interactions enables insights into atomic dy
 namics across a range of time and length scales but requires computation
 al simulation to analyse the spectral structure-property relationships. 
 However\, bridging the gap between the complexity of experimental sample
 s and the simplifications and approximations inherent in computational m
 odel systems is challenging to tackle. Our multi-scale ansatz starts wit
 h plane-wave density functional theory (DFT) to simulate NMR tensorial p
 roperties and their derived observables from first principles. DFT provi
 des the high-quality reference NMR tensors for tensorial machine learnin
 g (ML) in order to predict NMR with comparable computational efficiency 
 to long-timescale MD with machine learned interatomic potentials (MLIP).
  By combining MD simulations with ML-based NMR predictions\, NMR-relevan
 t dynamics over experimentally relevant timescales are directly simulate
 d capturing the evolution of structure–property relationships with high 
 accuracy. By the addition of the experimental postprocessing workflow\, 
 our approach opens the door to predictive in silico NMR experiments that
  reveal how local atomic environments govern macroscopic behaviour in co
 mplex materials. Finally\, the simulation of quantum dynamics enables us
  to customise NMR experiments and increase their selectivity for electro
 chemical interfaces. Bio: Simone Köcher studied chemistry at the Technic
 al University Munich with a focus on theoretical chemistry and magnetic 
 resonance. She conducted her PhD at IEK-9 Forschungszentrum Jülich and R
 WTH Aachen with Prof. Josef Granwehr in cooperation with Prof. Karsten R
 euter at TU Munich simulating lithium ion battery materials and computin
 g their spectroscopic as well as dynamic properties. After a PostDoc at 
 TU Munich working on parallel eigensolvers (ELPA) and their implementati
 on in electronic structure codes in collaboration with the Max Planck Co
 mputing and Data Facility (MPCDF)\, she joint Prof. Stefano Sanvito at T
 rinity College Dublin to study magnetic materials with first principles 
 as well as machine learning methods. In 2022\, she returned to the IEK-9
 \, now IET-1\, to head the new department of Theoretical Electrochemistr
 y and Data Science working on first principles simulations of material p
 roperties\, theoretical spectroscopy including quantum optimal control\,
  and digital image processing.
LOCATION:Lecture Theatre 0.04 IMC
CATEGORIES:WCPM
LAST-MODIFIED:20250908T114213Z
ORGANIZER;CN=Jin Kang:
END:VEVENT
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