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:20210916T132448Z
LOCATION:Michel Mayor
DTSTART;TZID=Europe/Stockholm:20210705T170000
DTEND;TZID=Europe/Stockholm:20210705T173000
UID:submissions.pasc-conference.org_PASC21_sess119_msa279@linklings.com
SUMMARY:Accurate Description of Long-Range Interaction Energy via Charge E
 quilibration Process
DESCRIPTION:Minisymposium\n\nAccurate Description of Long-Range Interactio
 n Energy via Charge Equilibration Process\n\nGhasemi\n\nLong-Range electro
 static energy is an essential part of total energy on an atomic scale. The
  standard machine learning methods such as high-dimensional neural network
  potentials take into account only short-range interactions. We present an
  approach to separate long-range and short-range interactions. The accurac
 y of the method can be systematically improved. The long-range contributio
 n is obtained within the framework of the charge equilibration process and
  the short-range interaction with the standard machine learning techniques
 . The method is illustrated for magnesium oxide clusters.\n\nDomain: Chemi
 stry and Materials
END:VEVENT
END:VCALENDAR