BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VTIMEZONE
TZID:Europe/Vienna
BEGIN:DAYLIGHT
DTSTART:20260329T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20261025T020000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20261013T074947Z
UID:1792659600@ist.ac.at
DTSTART:20261022T110000
DTEND:20261022T120000
DESCRIPTION:Speaker: William M. Jacobs\nhosted by Carl Goodrich\nAbstract: 
 Biological and bio-inspired materials derive function from sequence-encode
 d interactions in multicomponent mixtures. Predicting how biomolecular seq
 uences determine thermodynamic behavior and material properties is therefo
 re a central challenge across biophysics and molecular engineering. In thi
 s talk\, I introduce a machine-learning framework that maps sequences dire
 ctly to context-dependent chemical potentials—without requiring direct f
 ree-energy calculations. The approach learns low-dimensional\, physically 
 interpretable representations in which distances encode differences in the
 rmodynamic behavior across mixtures. Using intrinsically disordered protei
 ns as a model system\, I demonstrate how this framework enables quantitati
 ve prediction of partitioning and multicomponent phase diagrams\, while re
 vealing how sequence composition and patterning jointly control mixture th
 ermodynamics. More broadly\, this work outlines a path toward quantitative
  models of complex mixtures that bridge machine learning\, biophysics\, an
 d molecular engineering.
LOCATION:ISTA | Central Building | Ballroom \, ISTA
ORGANIZER:caroline.ernst-petz@ista.ac.at
SUMMARY:William M. Jacobs: Learning Thermodynamics from Sequence: Predictin
 g and Designing Complex Biomolecular Mixtures
URL:https://talks-calendar.ista.ac.at/events/6693
END:VEVENT
END:VCALENDAR