BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ZContent.net//ZapCalLib 1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
SUMMARY:Solubility Prediction with Graph Neural Networks: Data Hunger and C
 hemical Intuition
LOCATION:Chemistry A101
TZID:America/Denver
DTSTART:20231109T160000
UID:2026-04-16-12-00-02@natsci.colostate.edu
DTSTAMP:20260416T120002
Description:Abstract: \n\nSolubility represents a fundamental parameter wi
 th wide-ranging applications across diverse scientific domains\, including
  pharmaceuticals\, renewable energy\, and electrochemistry. Nevertheless\,
  the experimental determination of solubility faces substantial challenges
 \, particularly in scenarios involving ever-expanding solution combination
 s. In light of these experimental challenges\, computational methods have 
 gained prominence as viable means of solubility prediction. In this contex
 t\, this seminar will discuss two research papers that have made notable c
 ontributions to the field of solubility prediction\, specifically through 
 the utilization of graph neural networks. These papers offer significant a
 chievements in addressing critical issues related to data scarcity and the
  incorporation of chemical insights. Building on the motivations gained fr
 om these studies\, the seminar also outlines ongoing and prospective resea
 rch of mine\, aimed at enhancing chemical space coverage of data and expan
 ding the model to be applicable in multicomponent solvation systems. 4:00 
 pm
END:VEVENT
END:VCALENDAR