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Deep-Learning Database of Density Functional Theory Hamiltonians for Twisted Materials

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arxiv 2404.06449 v1 pith:FWU7WISI submitted 2024-04-09 cond-mat.mtrl-sci

Ting Bao , Runzhang Xu , He Li , Xiaoxun Gong , Zechen Tang , Jingheng Fu , Wenhui Duan , Yong Xu This is my paper · ORCID
classification cond-mat.mtrl-sci
keywords materialsdatabasetwistedddhtdeep-learningdensitye-twistedfunctional
verification ladder T0 review T1 audit T2 compute T3 formal
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Moir\'e-twisted materials have garnered significant research interest due to their distinctive properties and intriguing physics. However, conducting first-principles studies on such materials faces challenges, notably the formidable computational cost associated with simulating ultra-large twisted structures. This obstacle impedes the construction of a twisted materials database crucial for datadriven materials discovery. Here, by using high-throughput calculations and state-of-the-art neural network methods, we construct a Deep-learning Database of density functional theory (DFT) Hamiltonians for Twisted materials named DDHT. The DDHT database comprises trained neural-network models of over a hundred homo-bilayer and hetero-bilayer moir\'e-twisted materials. These models enable accurate prediction of the DFT Hamiltonian for these materials across arbitrary twist angles, with an averaged mean absolute error of approximately 1.0 meV or lower. The database facilitates the exploration of flat bands and correlated materials platforms within ultra-large twisted structures.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Organizing Principles for Moir\'e Quantum Matter

    cond-mat.mtrl-sci 2026-07 accept novelty 7.0 of 10

    Parent valley momentum, orbital content and moiré symmetry jointly organize emergent flat-band Hubbard, topological and quasi-1D models across all 2D lattice classes.

  2. Towards a universal model for spin-orbit coupled Wannier Hamiltonians

    cond-mat.mtrl-sci 2026-07 conditional novelty 6.0 of 10

    A single equivariant graph neural network generates spin-orbit coupled Wannier Hamiltonians for solids across 69 elements, backed by a post-processing package for subspace projection and linear-scaling spectral calculations.

  3. Long-Range Machine Learning of Electron Density for Twisted Bilayer Moir\'e Materials

    cond-mat.mtrl-sci 2026-02 conditional novelty 6.0 of 10

    Gaussian-process density prediction with long-range LOVV descriptors extrapolates from 3×3 displaced bilayers to twisted moiré supercells of >1000 atoms with meV-level band errors.

  4. MoireStudio: A Universal Twisted Electronic Structure Calculation Package

    cond-mat.mtrl-sci 2026-02 conditional novelty 5.0 of 10

    MoireStudio is a Python package that finds commensurate moiré angles, builds tight-binding and k·p Hamiltonians, and includes Fourier-based relaxation for twisted 2D materials.

  5. Review of the tight-binding method applicable to the properties of moir\'e superlattices

    cond-mat.mtrl-sci 2025-11 conditional

    A review of atomistic tight-binding Hamiltonians and numerical methods for moiré superlattices, with worked examples but no new research results.

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