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SUMMARY:Machine-learning interatomic potentials as computational technolog
 y - Alexander Shapeev\, Skoltech
DTSTART:20210128T140000Z
DTEND:20210128T150000Z
UID:TALK155008@talks.cam.ac.uk
CONTACT:Jan Behrends
DESCRIPTION:Over the past 15 years\, machine-learning interatomic potentia
 ls have evolved from a promising idea to a wide field of materials modelin
 g. The idea is that a true interatomic interaction energy (or an accurate 
 quantum-mechanical model of it) can be approximated as a function of posit
 ions of neighbors of each atom with a flexible (systematically improvable)
  functional form. Within this field\, researchers are working on different
  directions: studying ways to improve accuracy\, efficiency\, and the rang
 e of applicability of potentials\, constructing potentials for different a
 tomistic systems\, or combining these potentials with other algorithms to 
 enable materials properties calculation that was out-of-reach for more tra
 ditional methods. I will present my work in this field under my favorite a
 ngle: machine-learning potentials as a computational technology to seamles
 sly accelerate quantum-mechanical calculations.\n\nNamely\, I will present
  my version of machine-learning potentials\, moment tensor potentials\, an
 d an active learning algorithm automating the procedure of assembling the 
 dataset. I will show how the two algorithms combined allow for an automati
 c acceleration by orders of magnitude in such applications as constructing
  convex hulls of stable alloy structures or computing vibrational and conf
 igurational free energy of alloys. Moreover\, machine-learning potentials 
 can be used as a screening tool before the final quantum-mechanical calcul
 ation\, offering a speedup of several orders of magnitude without committi
 ng any numerical error.
LOCATION:Details of video conferencing will be distributed nearer the time
 .
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