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Deep Learning Sheds Light on Integer and Fractional Topological Insulators
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Deep Learning Sheds Light on Integer and Fractional Topological Insulators
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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 7 Pith papers
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Topological invariant of periodic many body wavefunction from charge pumping simulation
Charge-pumping simulation extracts Chern numbers and identifies anomalous composite Fermi liquids from neural network wavefunctions in fractional Chern insulators.
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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...
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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.
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