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PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260614T080859Z
UID:Seminar-DMML-1112@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Danushka Bollegala:MAILTO:Danushka.Bollegala@liverpool.ac.uk
DTSTART:20210407T110000
DTEND:20210407T120000
SUMMARY:Data Mining and Machine Learning Series
DESCRIPTION:Philip Smith: The Density Fingerprint of a Periodic Point Set\n\nCrystal Structure Prediction aims to reveal the properties that stable crystalline arrangements of a molecule have without stepping foot in a laboratory, consequently speeding up the discovery of new functional materials. Since it involves producing large datasets that themselves have little structure, an appropriate classification of crystals could add structure to these datasets and further streamline the process. We focus on geometric invariants, in particular introducing the density fingerprint of a crystal. After exploring its computations via Brillouin zones, we go on to show how it is invariant under isometries, stable under perturbations and complete at least for an open and dense space of crystal structures.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=1112
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