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SUMMARY:Machine learning potentials for molecular liquids - Max Veit\, Dep
 artment of Engineering\, University of Cambridge
DTSTART:20151111T110000Z
DTEND:20151111T113000Z
UID:TALK62286@talks.cam.ac.uk
CONTACT:Joseph Nelson
DESCRIPTION:Machine learning models systematically interpolate interatomic
  potentials from electronic structure calculations\, thereby exploiting th
 e smoothness of the Born-Oppenheimer potential energy surface. We can ther
 efore use such models to achieve the accuracy of DFT at a computational co
 st that is orders of magnitude smaller. I am exploring the application of 
 machine learning potentials to simulate molecular systems\, where interact
 ions between molecules\, which must be accounted for separately\, play a k
 ey role in determining the properties of the material. I will present the 
 progress of my work specifically to create a potential for liquid hydrocar
 bons with applications to fuels and lubricants research.
LOCATION:TCM Seminar Room\, Cavendish Laboratory
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