TY  - JOUR
A1  - Mertz, Thomas
A1  - Valentí, Roser
T1  - Engineering topological phases guided by statistical and machine learning methods
T2  - Physical review research
N2  - The search for materials with topological properties is an ongoing effort. In this article we propose a systematic statistical method, supported by machine learning techniques, that is capable of constructing topological models for a generic lattice without prior knowledge of the phase diagram. By sampling tight-binding parameter vectors from a random distribution, we obtain data sets that we label with the corresponding topological index. This labeled data is then analyzed to extract those parameters most relevant for the topological classification and to find their most likely values. We find that the marginal distributions of the parameters already define a topological model. Additional information is hidden in correlations between parameters. Here we present as a proof of concept the prediction of the Haldane model as the prototypical topological insulator for the honeycomb lattice in Altland-Zirnbauer (AZ) class A. The algorithm is straightforwardly applicable to any other AZ class or lattice, and could be generalized to interacting systems.
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/82368
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-823686
SN  - 2643-1564
VL  - 3
IS  - 1, Art. 013132
PB  - American Physical Society
CY  - College Park, MD
ER  -