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UID:DSC-22228
DTSTART;TZID=Europe/Berlin:20250915T140000
SEQUENCE:1757914687
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20250915T150000
URL:https://www.dresden-science-calendar.de/calendar/de/detail/22228
LOCATION:IFW\, Helmholtzstraße 2001069 Dresden
SUMMARY:Svynarskyi: Predicting critical temperature of superconductors via 
 machine learning methods
CLASS:PUBLIC
DESCRIPTION:Speaker: Ihor Svynarskyi\nInstitute of Speaker: Kyiv Academic U
 niversity\, Kyiv\nTopics:\n\n Location:\n  Name: IFW (B3E.26\, IFW Dresden
 )\n  Street: Helmholtzstraße 20\n  City: 01069 Dresden\n  Phone: \n  Fax:
  \nDescription: Superconductivity has been studied for over a century alre
 ady\, yet understanding the nature of high-temperature superconductivity r
 emains rather challenging. Using the 3DSC database\, which incorporates in
 formation on crystal structure of superconductors through special descript
 ors\, and integrating a fingerprint of electronic density of states into i
 t\, we address this problem with machine learning techniques. We explore d
 imensionality reduction via Principal Component Analysis\, unsupervised cl
 ustering of superconductors and finally prediction of the critical tempera
 ture with regression models\, namely K-nearest neighbours\, Random Forest\
 , and Gradient Boosting. Despite the limited data (fewer than 4000 compoun
 ds\, including around 900 HTSC)\, our models achieve competitive results\,
  leading the way to the search for new superconductors. .
DTSTAMP:20261010T070304Z
CREATED:20250829T053909Z
LAST-MODIFIED:20250915T053807Z
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