Exploration of Topological Band Structures using Deep Learning
DIPC Seminars
- Speaker
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Vittorio Peano, Max Planck Institute for the Science of Light, Germany
- When
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2022/03/25
13:00 - Place
- Donostia International Physics Center
- Add to calendar
-
iCal
The design of periodic nanostructures allows to tailor the transport of
photons, phonons, and matter waves for specific applications. Recent years
have seen a further expansion of this field by engineering topological
properties. However, what is missing currently are efficient ways to rapidly
explore and optimize band structures and to classify their topological
characteristics for arbitrary unit-cell geometries. In this work, we show how
deep learning can address this challenge. We introduce an approach where a
neural network first maps the geometry to a tight-binding model. The tight-
binding model encodes not only the band structure but also the symmetry
properties of the Bloch waves. This allows us to rapidly categorize a large
set of geometries in terms of their band representations, identifying designs
for fragile topologies. We demonstrate that our method is also suitable to
calculate strong topological invariants, even when (like the Chern number)
they are not symmetry indicated. Engineering of domain walls and optimization
are accelerated by orders of magnitude. Our method directly applies to any
passive linear material, irrespective of the symmetry class and space group.
It is general enough to be extended to active and nonlinear metamaterials.
Host: Dario Bercioux
ZOOM: