ELFT HÍRADÓ

[Janos Asboth <asboth.janos AT ttk.bme.hu>]

Meghívó

BME Elméleti Fizika Szeminárium,

dec. 1. péntek 10h15,

1111 Budapest, Budafoki út 8., BME F III. magasföldszint 01.,
Elméleti Fizika Tanszék szemináriumi szoba

Tim Mathies (Institute of Applied Physics and Interdisciplinary Nanoscience
Center Hamburg):
Classification of complex 2D magnetic ground states using unsupervised
contrastive learning

Phase diagrams capture the essential features of a system in many areas of
physics. Distinguishing one phase from another is often done by
hand-crafted selection rules and an automated approach could accelerate
this process. Here, we use a machine learning technique called contrastive
learning to classify 18,000 magnetic ground state configurations into 12
distinct clusters. This is done by using a hybrid approach of increasing
the number of clusters given by the model to 40 and then merging these
clusters into the 12 phases by hand. The ground states of two-dimensional
magnetic atomic lattices on metallic substrate are generated by fitting a
tight-binding model to a classical Heisenberg model and subsequent
classical Monte Carlo calculations. The symmetries of the system are
utilized as transformations to cluster identical phases together.
Furthermore, we investigate the representation space created by the model
as a quick overview for understanding large amounts of physical data.
Because of the lack of labeled phases, we judge the quality of the phase
diagrams by taking random samples of the resulting clusters. The approach
contributes to a better understanding of the connection between magnetism
and topological electronic matter. Our results are generalizable to the
automated identification of phases in condensed matter physics and beyond.

Minden érdeklődőt szeretettel várunk.
Asbóth János,
 szemináriumi koordinátor