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Neural Transformer Backflow for Solving Momentum-Resolved Ground States of Strongly Correlated Materials

Lixing Zhang, Di Luo

Abstract

Strongly correlated materials host a rich variety of exotic quantum phases but remain challenging to solve due to strong interactions. We introduce the Neural Transformer Backflow (NTB) framework, a powerful neural-network ansatz formulated within a multi-band projection formalism. NTB is mean-field transcendental, parameter-efficient and fermionic intrinsic, exhibiting superior performance compared with existing neural ansatzes. By naturally enforcing momentum conservation, NTB enables direct computation of momentum-resolved many-body ground states, providing detailed access to degeneracies and energy gaps. It achieves high accuracy on small systems and scales efficiently to larger sizes and higher-band truncations far beyond the reach of exact diagonalization. We demonstrate the power of NTB in capturing diverse correlated phases in twisted MoTe$_2$, including charge density waves, fractional Chern insulators, and anomalous Hall Fermi liquids, within a unified framework. This approach offers a generic, scalable route towards understanding and discovering quantum phases in strongly correlated materials.

Neural Transformer Backflow for Solving Momentum-Resolved Ground States of Strongly Correlated Materials

Abstract

Strongly correlated materials host a rich variety of exotic quantum phases but remain challenging to solve due to strong interactions. We introduce the Neural Transformer Backflow (NTB) framework, a powerful neural-network ansatz formulated within a multi-band projection formalism. NTB is mean-field transcendental, parameter-efficient and fermionic intrinsic, exhibiting superior performance compared with existing neural ansatzes. By naturally enforcing momentum conservation, NTB enables direct computation of momentum-resolved many-body ground states, providing detailed access to degeneracies and energy gaps. It achieves high accuracy on small systems and scales efficiently to larger sizes and higher-band truncations far beyond the reach of exact diagonalization. We demonstrate the power of NTB in capturing diverse correlated phases in twisted MoTe, including charge density waves, fractional Chern insulators, and anomalous Hall Fermi liquids, within a unified framework. This approach offers a generic, scalable route towards understanding and discovering quantum phases in strongly correlated materials.

Paper Structure

This paper contains 5 sections, 32 equations, 6 figures.

Figures (6)

  • Figure 1: Schematic of the architecture and applications of the Neural Transformer Backflow (NTB).
  • Figure 2: (a). Energy spectrum of momentum resolved ground states across different momentum sectors. 3-bands ED results are plotted against NTB results with 3-bands and 5-bands. (b). Optimization curve of $\Gamma$-point energy with $N_b = 3$ for different NN architectures. The gray dotted line is reference energy obtained from HF method. All networks are trained with $N_{\rm batch} = 10^{4}$. For (a) and (b), the parameters used are: $N_{\rm site} = 9$, $\nu = 2/3$, $\epsilon/\epsilon_0 = 10$, $\theta = 2.7^{\circ}$.
  • Figure 3: $\Gamma$-point energy across different choices of $\theta$. 1-band ED results are plotted against 2-bands NTB results. The parameters used are: $N_{\rm site} = 25$, $\nu = 3/5$, $\epsilon/\epsilon_0 = 5$.
  • Figure 4: Energy spectrum of momentum resolved ground states and the corresponding structure factor for filling factors $\nu=1/3$ and $\nu=2/3$. (a), (c): Structure factor calculated with $\Gamma$-point ground state. (b), (e): Energy spectrum of momentum resolved ground states at high symmetry points. The parameters used are: $N_{\rm site} = 27$, $N_b = 2$, $\epsilon/\epsilon_0 = 10$, $\theta = 2.7^{\circ}$.
  • Figure 5: Various of observables for filling factors $\nu= 1/4$ and $\nu= 3/4$. (a), (d): Structure factor at $\Gamma$-point. (b), (e): Momentum distribution function at $\Gamma$-point. (c), (f): Energy spectrum of momentum resolved ground states at high symmetry points. The parameters used are: $N_{\rm site} = 36$, $N_b = 1$, $\epsilon/\epsilon_0 = 10$, $\theta = 2.7^{\circ}$.
  • ...and 1 more figures