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VERSION:2.0
PRODID:-//University of Liverpool Computer Science Seminar System//v2//EN
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DTSTAMP:20260921T211058Z
UID:Seminar-MIF-1387@lxserverM.csc.liv.ac.uk
ORGANIZER:CN=Othon Michail:MAILTO:Othon.Michail@liverpool.ac.uk
DTSTART:20220513T140000
DTEND:20220513T150000
SUMMARY:MIF Series
DESCRIPTION:James Cumby : Encoding chemical similarity using pairwise distances.\n\nIn order to design new materials that fulfil a desired function, we require tools to rapidly predict physical properties from the underlying atomic structure. Statistical machine learning is accelerating advances in this area, but a key limitation remains; accurately predicting properties requires an effective measure of similarity between crystal structures. Here, we show that a representation based on grouped pairwise inter-atomic distances (GRID) combined with earth mover's distance (EMD) gives an accurate reflection of chemical space, and can be used to predict multiple physical properties using a simple regression model.\n\nhttps://www.csc.liv.ac.uk/research/seminars/abstract.php?id=1387
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