REVIEW 5 cited by
Deep Learning Sheds Light on Integer and Fractional Topological Insulators
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
Electronic topological phases of matter, characterized by robust boundary states derived from topologically nontrivial bulk states, are pivotal for next-generation electronic devices. However, understanding their complex quantum phases, especially at larger scales and fractional fillings with strong electron correlations, has long posed a formidable computational challenge. Here, we employ a deep learning framework to express the many-body wavefunction of topological states in twisted ${\rm MoTe_2}$ systems, where diverse topological states are observed. Leveraging neural networks, we demonstrate the ability to identify and characterize topological phases, including the integer and fractional Chern insulators as well as the $Z_2$ topological insulators. Our deep learning approach significantly outperforms traditional methods, not only in computational efficiency but also in accuracy, enabling us to study larger systems and differentiate between competing phases such as fractional Chern insulators and charge density waves. Our predictions align closely with experimental observations, highlighting the potential of deep learning techniques to explore the rich landscape of topological and strongly correlated phenomena.
Forward citations
Cited by 5 Pith papers
-
Topological excitonic insulators in electron bilayers modulated by twisted hBN
Hartree-Fock predicts that a twisted-hBN-spaced TMD bilayer at nu=1 can host a p-wave exciton condensate with coexisting quantum anomalous Hall and counterflow superfluid phases.
-
Machine learning the single-$\Lambda$ hypernuclei with neural-network quantum states
Neural-network quantum states with new spin and isospin treatments compute light hypernuclei spectra at claimed high accuracy and benchmark pionless effective field theory Hamiltonians.
-
Group Convolutional Neural Network for the Low-Energy Spectrum in the Quantum Dimer Model
Irrep-resolved GCNN variational energies indicate a 4-fold degenerate columnar ground state for V <= 0.4 in the square-lattice quantum dimer model, shifting possible plaquette or mixed ordering to 0.4 < V < 1.
-
Scaling universal Fermi network toward ground states: A diffusion-Monte-Carlo assessment
By scaling a universal fermionic neural network and certifying it with fixed-phase diffusion Monte Carlo, the paper shows the variational energy descending toward the exact ground state, with the DMC gap collapsing to...
-
Topological Order in Neural Wavefunctions
A neural-network variational wavefunction discovers a threefold-degenerate fractional Chern insulator state at ν=1/3 in a zero-net-flux periodic magnetic field, with degeneracy extracted from momentum projections.
Discussion (0). Continue with ORCID to comment.