BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VTIMEZONE
TZID:Europe/Vienna
BEGIN:DAYLIGHT
DTSTART:20240331T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20231029T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260424T143152Z
UID:1710838800@ist.ac.at
DTSTART:20240319T100000
DTEND:20240319T110000
DESCRIPTION:Speaker: Christoph Dellago\nhosted by Carl Goodrich\nAbstract: 
 The microscopic dynamics of many condensed matter systems occurring in nat
 ure and technology is dominated by rare but important barrier crossing eve
 nts. Examples of such processes include nucleation at first order phase tr
 ansitions\, chemical reactions and the folding of biopolymers. The resulti
 ng wide ranges of time scales are a challenge for molecular simulation and
  numerous simulation methods have been developed to address this problem. 
 Recently\, machine learning methods have been proposed as a powerful way t
 o further enhance such simulations. In my talk\, I will discuss various ma
 chine learning approaches based on deep neural networks to sample rare rea
 ctive trajectories and identify the collective variable needed for the con
 struction of low-dimensional models capturing the microscopic mechanism.
LOCATION:Moonstone Bldg / Ground floor / Seminar Room E\, ISTA
ORGANIZER:
SUMMARY:Christoph Dellago: Finding the needle in the haystack: machine lear
 ning for rare event simulations
URL:https://talks-calendar.ista.ac.at/events/4856
END:VEVENT
END:VCALENDAR