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arXiv · 2607.06053

Deep-learning Hamiltonian reveals twist-tunable flat bands and nonlinear photocurrents in SrTiO3 moire bilayers

Meiyang Yu·Chen Shen·Ruiwen Xie·Jingwei Tao·Lijun Zhang·Hongbin Zhang
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Abstract

The extension of moire physics to complex oxides offers new ways to manipulate electronic states, but the large oxide moire supercells make systematic first-principles calculations demanding. Here, we combine density functional theory with the E(3)-equivariant deep-learning Hamiltonian framework DeepH-E3 to investigate the twist-angle-dependent electronic structure and optical responses of twisted bilayer SrTiO3. The model is trained on untwisted bilayers with different interlayer-sliding configurations and then applied to commensurate twisted bilayers with twist angles from 8.80 degrees to 53.13 degrees. Compared with the untwisted bilayer, decreasing twist angle systematically flattens the valence bands and leads to nearly dispersionless bands at the smallest angles studied. Based on the predicted Hamiltonians, we evaluate the dielectric response, second-harmonic generation (SHG), shift current, and spin Hall conductivity. The dielectric response and spin Hall conductivity remain close to those of the untwisted bilayer, whereas the nonlinear optical responses are more strongly affected by twisting. SHG is strongly enhanced relative to the weak untwisted response, and the shift current shows a clear twist-angle dependence within the response-calculation range (53.13 degrees-22.62 degrees). These results show that twist engineering can control electronic and optoelectronic responses in oxide moire systems.

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Meiyang Yu, Chen Shen, Ruiwen Xie, Jingwei Tao, Lijun Zhang, Hongbin Zhang. 2026-07-07. Deep-learning Hamiltonian reveals twist-tunable flat bands and nonlinear photocurrents in SrTiO3 moire bilayers. https://arxiv.org/abs/2607.06053

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