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SUMMARY:Pushing Time Boundaries with Machine Learning Potentials - Profess
 or Marialore Sulpizi\, Ruhr University Bochum
DTSTART:20241030T143000Z
DTEND:20241030T153000Z
UID:TALK216484@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:Ab initio molecular dynamics simulations permit to explore str
 ucture and\ndynamics of complex systems including the full electronic stru
 cture\,\nhowever they suffer from severe timescale limitations. In the las
 t years\nmachine learning (ML) potentials have permitted to considerably s
 tretch\nthe timescale exploration pushing the ab initio accuracy beyond th
 ese\nlimits. In this talk I will present some examples from our recent\nre
 search activity\, where ML potentials\, based on ab initio data\, have\npe
 rmitted to address problems at the nanosecond scale. As a first\nexample I
  will discuss the conformational space of adenosine\nmonophosphate (AMP) i
 n explicit solution\, while as a second example\, I\nwill present the equi
 librium structure of the electric double layer at a\ndefective metal/water
  interface also including the acid dissociation\nequilibrium.\nChallenges\
 , limitations and perspectives will be also discussed.\n
LOCATION:Zoom link: https://cam-ac-uk.zoom.us/j/84563697086?pwd=CxsjV49w2i
 3aXVJ1NaB7blbUjKI4gE.1
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