Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session F36: Molecular Dynamics Ex Machina: Successes and Challenges
8:00 AM–11:00 AM,
Tuesday, March 3, 2020
Room: 601/603
Sponsoring
Unit:
DCOMP
Chair: Boris Kozinsky, Harvard University
Abstract: F36.00005 : Machine-learning interatomic potentials: a story about how a Big Data approach compensates for our incomplete understanding of interatomic interaction*
Presenter:
Alexander Shapeev
(Skolkovo Institute of Science and Technology)
Author:
Alexander Shapeev
(Skolkovo Institute of Science and Technology)
Namely, in the first part of my presentation I will give a brief introduction to machine-learning potentials and present some of their success stories. The second part will be devoted to methodological foundations of machine-learning potentials and their successes. In particular, I will formulate the problem of construction of an interatomic potential as a model reduction problem with respect to a quantum-mechanical model (such as DFT). I will then discuss what modeling assumptions are made in classical potentials, identify which ones cause uncontrollable errors, and show how machine learning helps to lift some of those assumptions while still benefiting from physical knowledge. I will conclude by discussing the existing challenges related to those modeling assumptions that are difficult to lift or those cases in which common assumptions fail.
*This work is supported by the Russian Science Foundation (Grant No. 18-13-00479).
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