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BEGIN:VEVENT
DTSTAMP:20260306T063049Z
DTSTART;VALUE=DATE-TIME:20191007T130000
DTEND;VALUE=DATE-TIME:20191007T140000
SUMMARY:Albert Bartok Partay (Warwick Physics/Eng)
TZID:Europe/London
UID:20191007-8a1785d76d2192c0016d2b0935166d5f@warwick.ac.uk
CREATED:20190930T184729Z
DESCRIPTION:Learning atomic interactions from microscopic observables Des
 igning new materials needs a detailed understanding of the structure and
  processes of matter on the atomistic scale\, because all macroscopic pr
 operties ultimately depend on microscopic interactions. For such studies
 \, quantum mechanical modelling combined with atomistic simulations has 
 been proven to be predictive in addition to being able to explain experi
 mental phenomena. However\, larger length and timescales are not easily 
 accessible due to the non-linear growth in computational resources requi
 red to numerically solve the quantum mechanical equations. We would like
  to enable fast simulations withouta compromise in accuracy by using mac
 hine learning techniques to fit the quantum mechanical model. To realise
  this aim\, we have developed the Gaussian Approximation Potentials fram
 ework\, which uses microscopic data from quantum mechanical calculations
  on small systems to create fast\, accurate and scalable models. Apart f
 rom data\, the other main ingredient needed to fit Gaussian Processes ar
 e kernels. In my talk I will discuss kernels that are designed to compar
 e atomic structures and show examples from molecular and condensed matte
 r systems. These kernels are used to define a set of interatomic potenti
 als or models\, and a Bayesian approach determines which is the most lik
 ely\, based on the data as evidence.
LOCATION:D2.02 (Engineering)
CATEGORIES:
LAST-MODIFIED:20190930T184729Z
ORGANIZER;CN=James Kermode:
END:VEVENT
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