BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:From Data to Membranes: Predicting Performance and Guiding Materia l Selection with Machine Learning DTSTART:20250716T143000Z DTEND:20250716T160000Z DTSTAMP:20260602T212926Z UID:b0aa759a-51aa-4e0c-9eac-1b22c4483f6c SEQUENCE:2 CREATED:20250704T083842Z DESCRIPTION:The integration of machine learning (ML) techniques into membr ane research offerspromising possibilities for accelerating material disco very and enhancing processperformance prediction. In this talk\, I will fi rst provide a brief motivation for the use ofML in membrane development\, followed by an overview of key ML concepts andcommon methods for regressio n and classification tasks. Two case studies willillustrate the practical application of these tools. The first explores the use of ML topredict gas permeability in polymeric membranes and leverages these predictions toide ntify promising new materials for gas separation. The second applies ML mo dels intwo different tasks: to identify and estimate the distribution of p ores\, and to forecastthe long-term performance of membranes used in membr ane distillation processes\,offering a data-driven approach to system moni toring and optimization. Theseexamples highlight how ML can complement exp erimental efforts and supportdecision-making in membrane research. LAST-MODIFIED:20250704T083859Z LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/from-data-to-membranes-pre dicting-performance-and-guiding-material-selection-with-machine-learning/ X-ALT-DESC;FMTTYPE=text/html:

The integration of machine learning (ML) techniques into membrane research offerspromising po ssibilities for accelerating material discovery and enhancing processperfo rmance 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.

Two case studies willillustrate the practical application of these tools. The first explores the use of ML topredict gas permeability in poly meric membranes and leverages these predictions toidentify promising new m aterials for gas separation.

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 distill ation processes\,offering a data-driven approach to system monitoring and optimization. Theseexamples highlight how ML can complement experimental e fforts and supportdecision-making in membrane research.

END:VEVENT END:VCALENDAR