BEGIN:VCALENDAR
PRODID:-//SiteBuilder 2//University of ÌÇÐÄTV ITS Web Team//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-TIMEZONE:Europe/London
X-LIC-LOCATION:Europe/London
BEGIN:VTIMEZONE
TZID:Europe/London
LAST-MODIFIED:20201010T011803Z
TZURL:http://tzurl.org/zoneinfo/Europe/London
X-LIC-LOCATION:Europe/London
X-PROLEPTIC-TZNAME:LMT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+000115
TZOFFSETTO:+0000
DTSTART:18471201T000000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19160521T020000
RDATE:19170408T020000
RDATE:19180324T020000
RDATE:19190330T020000
RDATE:19200328T020000
RDATE:19210403T020000
RDATE:19220326T020000
RDATE:19230422T020000
RDATE:19240413T020000
RDATE:19270410T020000
RDATE:19300413T020000
RDATE:19330409T020000
RDATE:19340422T020000
RDATE:19350414T020000
RDATE:19380410T020000
RDATE:19390416T020000
RDATE:19400225T020000
RDATE:19460414T020000
RDATE:19470316T020000
RDATE:19480314T020000
RDATE:19490403T020000
RDATE:19530419T020000
RDATE:19540411T020000
RDATE:19570414T020000
RDATE:19600410T020000
RDATE:19680218T020000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19161001T030000
RDATE:19170917T030000
RDATE:19180930T030000
RDATE:19190929T030000
RDATE:19201025T030000
RDATE:19211003T030000
RDATE:19221008T030000
RDATE:19391119T030000
RDATE:19471102T030000
RDATE:19481031T030000
RDATE:19491030T030000
RDATE:19711031T030000
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19230916T030000
RRULE:FREQ=YEARLY;UNTIL=19240921T020000Z;BYMONTH=9;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19250419T020000
RRULE:FREQ=YEARLY;UNTIL=19260418T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19251004T030000
RRULE:FREQ=YEARLY;UNTIL=19381002T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19280422T020000
RRULE:FREQ=YEARLY;UNTIL=19290421T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19310419T020000
RRULE:FREQ=YEARLY;UNTIL=19320417T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19360419T020000
RRULE:FREQ=YEARLY;UNTIL=19370418T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BDST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19410504T020000
RDATE:19450402T020000
RDATE:19470413T020000
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19410810T030000
RRULE:FREQ=YEARLY;UNTIL=19430815T010000Z;BYMONTH=8;BYMONTHDAY=9,10,11,12,
 13,14,15;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BDST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19420405T020000
RRULE:FREQ=YEARLY;UNTIL=19440402T010000Z;BYMONTH=4;BYMONTHDAY=2,3,4,5,6,7
 ,8;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19440917T030000
RDATE:19450715T030000
RDATE:19470810T030000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19451007T030000
RRULE:FREQ=YEARLY;UNTIL=19461006T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19500416T020000
RRULE:FREQ=YEARLY;UNTIL=19520420T020000Z;BYMONTH=4;BYMONTHDAY=14,15,16,17
 ,18,19,20;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19501022T030000
RRULE:FREQ=YEARLY;UNTIL=19521026T020000Z;BYMONTH=10;BYMONTHDAY=21,22,23,2
 4,25,26,27;BYDAY=SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19531004T030000
RRULE:FREQ=YEARLY;UNTIL=19601002T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19550417T020000
RRULE:FREQ=YEARLY;UNTIL=19560422T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19580420T020000
RRULE:FREQ=YEARLY;UNTIL=19590419T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19610326T020000
RRULE:FREQ=YEARLY;UNTIL=19630331T020000Z;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19611029T030000
RRULE:FREQ=YEARLY;UNTIL=19671029T020000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19640322T020000
RRULE:FREQ=YEARLY;UNTIL=19670319T020000Z;BYMONTH=3;BYMONTHDAY=19,20,21,22
 ,23,24,25;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:BST
TZOFFSETFROM:+0100
TZOFFSETTO:+0100
DTSTART:19681026T230000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19720319T020000
RRULE:FREQ=YEARLY;UNTIL=19800316T020000Z;BYMONTH=3;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19721029T030000
RRULE:FREQ=YEARLY;UNTIL=19801026T020000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19810329T010000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19811025T020000
RRULE:FREQ=YEARLY;UNTIL=19891029T010000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19901028T020000
RRULE:FREQ=YEARLY;UNTIL=19951022T010000Z;BYMONTH=10;BYDAY=4SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
DTSTART:19960101T000000
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19961027T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260427T183707Z
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
END:VCALENDAR