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DTSTART;VALUE=DATE:20261105T101500
DTEND;VALUE=DATE:20261105T110000
UID:20563@agenda.unifr.ch
DESCRIPTION:Talk as part of a colloquium in Data-Driven Materials Discovery\n\nSummary: Data-driven materials discovery promises to accelerate the design of soft, polymeric and bio-inspired materials, but real materials data are often limited, heterogeneous and incomplete. In this talk, I will first introduce the basic idea of learning structure–property relationships in materials and explain why physical knowledge, uncertainty and interpretability are essential. I will then present my research on machine-learning methods for polymer glass transition, molecular-simulation data analysis, and experimental soft-matter data, including collaborations with academic and industrial partners. Finally, I will outline future plans at AMI: materials informatics for limited-data regimes, reconstruction of hidden structural ensembles from measurable properties, and uncertainty-aware workflows toward sustainable and functional soft materials.\n
SUMMARY:From Limited Data to Materials Discovery: Machine Learning for Soft and Polymeric Materials
CATEGORIES:Colloque / Congrès / Forum
LOCATION:PER 18\, Auditorium C 322\, Chemin des Verdiers 4\, 1700 Fribourg
URL;VALUE=URI:https://agenda.unifr.ch/e/fr/20563
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