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SUMMARY:Models for atomistic simulations: beyond interatomic potentials - 
 Filippo Bigi\, EPFL\, Switzerland
DTSTART:20251103T140000Z
DTEND:20251103T143000Z
UID:TALK239740@talks.cam.ac.uk
CONTACT:Dr Fabian Berger
DESCRIPTION:Machine-learned interatomic potentials have greatly enhanced t
 he efficiency and scope of atomistic simulations. Beyond learning potentia
 l energy surfaces\, modeling other physical quantities can yield further a
 cceleration. As one example\, we will discuss the direct prediction of int
 eratomic forces\, which achieves a two- to threefold speedup compared to c
 omputing forces as derivatives of an energy model. When employed with care
 \, simulations based on such force models can reproduce physical observabl
 es with full fidelity. Building on this idea\, we introduce both symmetry-
 preserving and symmetry-free strategies for long-time-step molecular dynam
 ics\, in which future atomic positions and momenta are predicted directly.
  This emerging paradigm enables up to two orders of magnitude acceleration
  relative to standard machine-learned potentials\, dramatically extending 
 the accessible time scales in atomistic models.\n
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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