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DTSTART;VALUE=DATE:20261106T111500
DTEND;VALUE=DATE:20261106T120000
UID:20568@agenda.unifr.ch
DESCRIPTION:Talk as part of a colloquium in Data-Driven Materials discovery.\n\nSummary: Materials discovery is full of patterns that matter, but that we do not yet know how to write down. They appear in spectra, recipes, images, simulations, papers, and in the tacit judgment of experimentalists. In this talk, I will discuss how my group builds machine-learning systems that try to recover such patterns from heterogeneous scientific data, turn them into predictions, and test whether those predictions are useful. Examples will include chemical reasoning benchmarks, multimodal models, spectroscopy-to-structure inference, and recipe-aware prediction. The central question is not whether a model can produce an answer, but whether it used the right evidence and whether the answer can change what a scientist does next. The goal is to build reliable data-driven tools that make materials discovery more systematic, transparent, and actionable.\n\n
SUMMARY:Learning What Matters: From Materials Data to Scientific Decisions
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/20568
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