BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20210916T132449Z
LOCATION:Michel Mayor
DTSTART;TZID=Europe/Stockholm:20210706T113000
DTEND;TZID=Europe/Stockholm:20210706T120000
UID:submissions.pasc-conference.org_PASC21_sess126_msa242@linklings.com
SUMMARY:Large-Scale GW Calculations Employing Sparse-Tensor Operations
DESCRIPTION:Minisymposium\n\nLarge-Scale GW Calculations Employing Sparse-
 Tensor Operations\n\nWilhelm\n\nIn traditional <em>GW</em> implementations
 , the computational cost is growing as <em>O</em>(<em>N^</em>4) in the sys
 tem size <em>N</em>, which prohibits their application to many systems of 
 interest. I present a <em>GW</em> algorithm in a Gaussian-type basis with 
 a computational cost scales with <em>N^</em>2 to <em>N^</em>3. The algorit
 hm makes use of massively parallel sparse-tensor operations employing the 
 DBCSR library. It will be shown that large minimax grids and resolution of
  the identity with the truncated Coulomb metric improve the accuracy of th
 e low-scaling <em>GW </em>algorithm to < 0.01 eV for the <em>GW</em>100
  test set. Large-scale applications of low-scaling GW will be discussed.\n
 \nDomain: CS and Math, Chemistry and Materials, Physics, Engineering
END:VEVENT
END:VCALENDAR