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UID:DSC-17389
DTSTART;VALUE=DATE:20200817
SEQUENCE:1597739355
TRANSP:OPAQUE
DTEND;VALUE=DATE:20200822
URL:https://dresden-science-calendar.de/calendar/en/detail/17389
LOCATION:Online\,   
SUMMARY:Summer School \"Materials Genome Engineering\"
CLASS:PUBLIC
DESCRIPTION:Speaker: \nInstitute of Speaker: \nTopics:\nPhysik\, Informatik
 \, Chemie\, Elektro- u. Informationstechnik\, Weiterbildung\, Ausgründung
 /Transfer\nTime:\n2:00 PM-7:00 PM\n\n Location:\n  Name: Online ()\n  Stre
 et:  \n  City:  \n  Phone: \n  Fax: \nDescription: Innovative materials ar
 e one of the key technologies for keeping products and industrial processe
 s economically competitive and ecologically sustainable. Modern materials 
 science requires a multi-discipline approach embracing chemistry\, physics
 \, engineering\, as well as data science. This summer school will provide 
 an overview of current developments in data-driven materials science invol
 ving Machine Learning and modern numerical techniques and will offer a pla
 tform for discussions about future perspectives.    Materials innovations 
 enable new technological capabilities and drive major societal advancement
 s but typically require long and costly development cycles. Materials Geno
 me Engineering aims at realizing the transition to a new paradigm in mater
 ials development from a traditional \"trial and error\" mode to a \"ration
 ally designed experiments\" mode. In this highly promising approach\, thor
 ough and reliable theoretical prediction and high-throughput screening are
  followed by experimental verification and technological implementation. C
 omplementary efforts and the seamless integration of theory\, computation 
 and experiment\, will result in a remarkable acceleration of the pace of n
 ew materials discovery\, design and deployment. To fully take advantage of
  its potential\, this novel scheme has to rely on cross-innovation and con
 vergence of various scientific fields such as materials science\, computer
  science\, physics\, chemistry as well as information and data science.   
  This online summer school targets Master students\, Ph.D. students and (e
 arly-stage) Postdocs interested in or working on topics related to computa
 tional and experimental high-throughput approaches\, machine learning and 
 data-driven materials science.
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