Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session NN00: Virtual Poster Session III (6:30am-8:00am CST)
6:30 AM,
Thursday, March 7, 2024
Chair: Abdullah AlShuaibi, Cornell University; Apurba Paul, University of Notre Dame; Jonte Hance, Newcastle University
Abstract: NN00.00014 : Completeness of representations in atomistic machine learning*
Presenter:
Jigyasa Nigam
(Ecole Polytechnique Federale de Lausanne)
Authors:
Jigyasa Nigam
(Ecole Polytechnique Federale de Lausanne)
Michele Ceriotti
(Ecole Polytechnique Federale de Lausanne)
Sergey Pozdnyakov
(EPFL)
Kevin K Huguenin-Dumittan
(EPFL)
However, the descriptors that are commonly used to represent point clouds -- most notably those adopted to describe matter at the atomic scale -- are unable to distinguish between special arrangements of particles in three dimensions. This makes it impossible to machine learn their properties. Frameworks that are provably complete exist but are only so in the limit in which they simultaneously describe the mutual relationship between all atoms, which is impractical. I will present a class of descriptors based on finite correlations based on the relative arrangement of particle triplets, which can be employed to create symmetry-adapted models with universal approximation capabilities and provide a solution to the problem of complete descriptors for machine learning
*JN acknowledges funding from the European Research Council Grant, FIAMMA and the National Center for Competence in Research MARVEL funded by the Swiss National Science Foundation
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