Multi-faceted machine learning of competing orders in disordered interacting systems
| Autor: | Michael Matty, Yi Zhang, Zlatko Papic, Eun-Ah Kim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Physics
Strongly Correlated Electrons (cond-mat.str-el) Artificial neural network Computer Science::Neural and Evolutionary Computation Phase (waves) FOS: Physical sciences Disordered Systems and Neural Networks (cond-mat.dis-nn) Quantum Hall effect Condensed Matter - Disordered Systems and Neural Networks 01 natural sciences 010305 fluids & plasmas Condensed Matter - Strongly Correlated Electrons 0103 physical sciences Topological order Symmetry breaking Statistical physics 010306 general physics Charge density wave Phase diagram |
| ISSN: | 2469-9950 |
| Popis: | While the non-perturbative interaction effects in the fractional quantum Hall regime can be readily simulated through exact diagonalization, it has been challenging to establish a suitable diagnostic that can label different phases in the presence of competing interactions and disorder. Here we introduce a multi-faceted framework using a simple artificial neural network (ANN) to detect defining features of a fractional quantum Hall state, a charge density wave state and a localized state using the entanglement spectra and charge density as independent input. We consider the competing effects of a perturbing interaction ($l = 1$ pseudopotential $\Delta V_1$), a disorder potential $W$, and the Coulomb interaction to the system at filling fraction ${\nu} = 1/3$. Our phase diagram benchmarks well against previous estimates of the phase boundary using conventional measures along the $\Delta V_1 = 0$ and $W = 0$ axes, the only regions where conventional approaches have been explored. Moreover, exploring the entire two-dimensional phase diagram for the first time, we establish the robustness of the fractional quantum Hall state and map out the charge density wave micro-emulsion phase wherein droplets of charge density wave region appear before the charge density wave is completely disordered. Hence we establish that the ANN can access and learn the defining traits of topological as well as broken symmetry phases using multi-faceted inputs of entanglement spectra and charge density. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|