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SUMMARY:Machine learning force fields shows extreme generalisation - Profe
 ssor Gabor Csanyi
DTSTART:20251021T170000Z
DTEND:20251021T183000Z
UID:TALK236986@talks.cam.ac.uk
CONTACT:129301
DESCRIPTION:I will introduce the general problem of first principles force
  fields: creating surrogate models for quantum mechanics that yield the en
 ergy of a configuration of atoms in 3D space\, as we would find them in ma
 terials or molecules. Over the last decade significant advances were made 
 in the attainable accuracy\, and today we can model materials and molecule
 s with a per-atom energy accuracy of up to 1 part in 10\,000 with a speedu
 p of over a million or more compared to the explicit quantum mechanical ca
 lculation\, enabling accurate molecular dynamics simulations on large leng
 th and time scales. The most surprising aspect of the best models is its e
 xtreme generalisation: fitted only on small periodic crystals\, it shows s
 table trajectories on arbitrary chemical systems\, from water to nanoparti
 cles and proteins. The precise relationship between the architectural elem
 ents and the extreme generalisation is still a mystery. The locality of th
 e graph neural network structure is key to its success\, as well as high b
 ody order and message passing. The force fields get significantly better w
 ith more data\, yet model size and complexity can remain largely the same.
  Current challenges include integrating explicit long range electrostatics
  and combining large datasets for materials and organic molecules where th
 e appropriate levels of electronic structure theory are incompatible.\n\nT
 alks are priced at £4 for non-Scientific Society members. Scientific Soci
 ety members will have free access to all our talks. Lifetime membership co
 sts £15 and gives free access to all talks\, members-only events and prio
 rity access to oversubscribed SciSoc events.
LOCATION:Wolfson Lecture Theatre\,  Department of Chemistry\, Lensfield Ro
 ad
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