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nomad-lab
analytics-toolkit-tutorials
Commits
e2ab723d
Commit
e2ab723d
authored
Feb 04, 2018
by
Luca Ghiringhelli
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Edited QSHI yaml
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topological-quantum-phases/QSHI_trivial.demoinfo.yaml
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topological-quantum-phases/QSHI_trivial.demoinfo.yaml
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e2ab723d
title
:
"
Prediction
of
topological
quantum
phase
transitions"
logicalPath
:
"
/data/shared/tutorialsNew/topological-quantum-phases/QSHI_trivial.bkr"
authors
:
[
"
Ahmetcik,
Emre"
,
"
Ziletti,
Angelo"
,
"
Ouyang,
Runhai
"
,
"
Ghiringhelli,
Luca"
,
"
Scheffler,
Matthias"
]
authors
:
[
"
Mera
Acosta,
Carlos"
,
"
Ahmetcik,
Emre"
,
"
Carbogno,
Christian"
,
"
Ouyang,
Runhai"
,
"
Fazzio,
Adalberto
"
,
"
Ghiringhelli,
Luca"
,
"
Scheffler,
Matthias"
]
editLink
:
"
/notebook-edit/data/shared/tutorialsNew/topological-quantum-phases/QSHI_trivial.bkr"
isPublic
:
true
username
:
"
tutorialsNew"
...
...
@@ -8,7 +8,7 @@ description: >
This tutorial shows how to find descriptive parameters (short formulas) for the prediction of
topological phase transitions. As an example, we address the topological classification of
two-dimensional functionalized honeycomb-lattice materials, which are formally described by
the
$Z_2$ topological invariant, i.e., $Z_2=0$ for trivial (normal) insulators and $Z_2=1$
for two-dimensional
the
Z2 topological invariant, i.e., Z2=0 for trivial (normal) insulators and Z2=1
for two-dimensional
topological insulators (quantum spin Hall insulators).
Using a recently developed machine learning based on compressed sensing, we then derive a map of
these materials, in which metals, trivial insulators, and quantum spin Hall insulators are separated
...
...
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