diff --git a/topological-quantum-phases/QSHI_trivial.demoinfo.yaml b/topological-quantum-phases/QSHI_trivial.demoinfo.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33eeab98cb740babce23059e5df75f6852c60aca --- /dev/null +++ b/topological-quantum-phases/QSHI_trivial.demoinfo.yaml @@ -0,0 +1,35 @@ +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"]