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VERSION:2.0
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SUMMARY:From Data to Membranes: Predicting Performance and Guiding Materia
 l Selection with Machine Learning
DTSTART:20250716T143000Z
DTEND:20250716T160000Z
DTSTAMP:20260606T034035Z
UID:b0aa759a-51aa-4e0c-9eac-1b22c4483f6c
SEQUENCE:1
CREATED:20250704T083831Z
DESCRIPTION: The integration of machine learning (ML) techniques into memb
 rane research offerspromising possibilities for accelerating material disc
 overy and enhancing processperformance prediction. In this talk\, I will f
 irst provide a brief motivation for the use ofML in membrane development\,
  followed by an overview of key ML concepts andcommon methods for regressi
 on and classification tasks. Two case studies willillustrate the practical
  application of these tools. The first explores the use of ML topredict ga
 s permeability in polymeric membranes and leverages these predictions toid
 entify promising new materials for gas separation. The second applies ML m
 odels intwo different tasks: to identify and estimate the distribution of 
 pores\, and to forecastthe long-term performance of membranes used in memb
 rane distillation processes\,offering a data-driven approach to system mon
 itoring and optimization. Theseexamples highlight how ML can complement ex
 perimental efforts and supportdecision-making in membrane research. 
LAST-MODIFIED:20250704T083831Z
LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building
URL:http://df.vps.tecnico.ulisboa.pt/en/events/from-data-to-membranes-pred
 icting-performance-and-guiding-material-selection-with-machine-learning/
X-ALT-DESC;FMTTYPE=text/html:<p data-block-key="dep0f"> The integration of
  machine learning (ML) techniques into membrane research offerspromising p
 ossibilities for accelerating material discovery and enhancing processperf
 ormance prediction. In this talk\, I will first provide a brief motivation
  for the use ofML in membrane development\, followed by an overview of key
  ML concepts andcommon methods for regression and classification tasks. <b
 r/><br/>Two case studies willillustrate the practical application of these
  tools. The first explores the use of ML topredict gas permeability in pol
 ymeric membranes and leverages these predictions toidentify promising new 
 materials for gas separation. <br/><br/>The second applies ML models intwo
  different tasks: to identify and estimate the distribution of pores\, and
  to forecastthe long-term performance of membranes used in membrane distil
 lation processes\,offering a data-driven approach to system monitoring and
  optimization. Theseexamples highlight how ML can complement experimental 
 efforts and supportdecision-making in membrane research. </p>
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