adding topological quantum phases predition (of Emre)

parent bc3018ac
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
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: [""]
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