Skip to content
GitLab
Projects
Groups
Snippets
Help
Loading...
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
A
analytics-toolkit-tutorials
Project overview
Project overview
Details
Activity
Releases
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Issues
1
Issues
1
List
Boards
Labels
Service Desk
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Operations
Operations
Environments
Packages & Registries
Packages & Registries
Container Registry
Analytics
Analytics
CI / CD
Repository
Value Stream
Wiki
Wiki
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
nomad-lab
analytics-toolkit-tutorials
Commits
29e13122
Commit
29e13122
authored
Feb 04, 2018
by
Mohamed, Fawzi Roberto (fawzi)
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
adding topological quantum phases predition (of Emre)
parent
bc3018ac
Changes
1
Hide whitespace changes
Inline
Side-by-side
Showing
1 changed file
with
35 additions
and
0 deletions
+35
-0
topological-quantum-phases/QSHI_trivial.demoinfo.yaml
topological-quantum-phases/QSHI_trivial.demoinfo.yaml
+35
-0
No files found.
topological-quantum-phases/QSHI_trivial.demoinfo.yaml
0 → 100644
View file @
29e13122
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"
]
editLink
:
"
/notebook-edit/data/shared/tutorialsNew/topological-quantum-phases/QSHI_trivial.bkr"
isPublic
:
true
username
:
"
tutorialsNew"
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
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
in different spatial domains.
The axes of this map are given by a physically meaningful descriptor, i.e., a non-linear analytic
function that only depends on the properties of the material's constituent atoms, but not on the properties
of the material itself.
The method is based on the algorithm sure independence screening and sparsifying operator (SISSO),
which enables to search for optimal descriptors by scanning huge feature spaces.
created_at
:
"
2017-11-13T13:56:28.375Z"
updated_at
:
"
2017-11-13T13:56:28.375Z"
user_update
:
"
2018-01-30"
top_of_list
:
false
featured
:
true
labels
:
category
:
[
"
Demo"
]
platform
:
[
"
beaker"
]
language
:
[
"
python"
,
"
javascript"
]
data_analytics_method
:
[
"
Compressed
Sensing"
,
"
SISSO"
]
application_section
:
[
"
Materials
property
prediction"
]
application_keyword
:
[
"
Qunatum
Phase"
,
"
Topological
insulator"
,
"
Classification"
]
visualization
:
[
"
NOMAD
viewer"
]
reference
:
[
"
https://arxiv.org/abs/1710.03319"
]
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment