MoireStudio 1.0.4

Universal Twisted Electronic Structure Calculation Package

General Toolkit for Twisted / Moiré Electronic Structure: MoireStudio

This repository provides a general-purpose toolkit for twisted / moiré (moire) systems, including both k·p and tight-binding (TB/Wannier) workflows, plus geometry utilities (commensurate-angle scan, moiré supercell generation) and basic visualization.

Target users: theorists and computational researchers working on 2D materials and twisted/moire systems.
Platforms: Linux / macOS / Windows (including WSL).

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Features

• k·p workflow

  • Band structures along high-symmetry paths
  • Chern number computation on k-meshes

• TB / Wannier workflow

  • POSCAR-driven TB models (structure-based)
  • Wannier90-driven models (using .win / _hr.dat)

• Geometry module

  • Real-space moiré point distribution
  • Commensurate-angle scan
  • Moiré supercell generation (POSCAR)

• Visualization

  • Band plots
  • Moiré lattice point plots
  • Commensurate-angle distribution plots

• Parallelism & cross-platform

  • Process/thread-based parallel backends with BLAS thread pinning
  • Windows-friendly multiprocessing (uses spawn mode)

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Environment & Installation

• Python ≥ 3.9
• Required packages (minimal):
  • numpy
  • scipy
  • matplotlib

Install via:

pip install numpy scipy matplotlib

For better linear-algebra performance, a NumPy/SciPy build with MKL or another optimized BLAS is recommended.

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Quick Start

From the project root:

python driver.py --input input.json

The main calculation mode is selected by the top-level JSON key "mode" and sub-key "task":

• kp.band – k·p band structure
• kp.chern – k·p Chern number
• tb.band – TB/Wannier band structure (POSCAR or Wannier90)
• struc.find_com – scan commensurate twist-angle distribution
• struc.gen_pos – generate a moiré supercell (POSCAR + schematic plot)

Default output directory: ./outputs (configurable via io.output_dir).

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Recommended Project Layout

project/
  driver.py
  solver.py
  twist_tb.py
  twist_kp.py
  twist_geometry.py
  read_tb_model.py
  twist_viz.py
  inputs/
    POSCAR                 # or Wannier90: *.win / *_hr.dat / FERMI_ENERGY
  outputs/                 # generated after running

• Wannier90 input: requires *.win and *_hr.dat.
  • Fermi level can be provided via inputs/FERMI_ENERGY or via tb.wannier.fermi_energy.
• POSCAR input: single-layer or bilayer structures are supported (controlled by struc.read_layer).

---

Input File input.json

Top-level structure

{
  "mode": "kp | tb | struc",
  "task": "...",
  "io":   { ... },
  "kp":   { ... },
  "tb":   { ... },
  "struc":{ ... }
}

Common section io

• output_dir: output directory (default ./outputs)
• input_dir: input directory (default ./inputs)
• fig_band: filename for band plot (default <task>.png)
• fig_moire: filename for moiré-point plot (default moire_points.png)
• fig_comm: filename for commensurate-angle plot (default comm_angles.png)
• show_plots: whether to show figures interactively (true/false)
• Heterobilayer (optional):
  • t_prefix / b_prefix: Wannier file prefixes for top/bottom layers
  • t_fermi_energy / b_fermi_energy: corresponding Fermi energies

Geometry & Supercell struc

• read_mode: "POSCAR" or "model" (Wannier)
• read_layer: "bilayer" / "monolayer" (only in POSCAR mode)
• ihetero: whether the system is heterobilayer (true/false)
• pos_path / pos_prefix: path and prefix for POSCAR input
• d_AA / d_AB: out-of-plane AA/AB interlayer distances (units consistent with input)

Supercell generation & search:

• zero_point: reference lattice indices for moiré center
• lim, search, accurate, if_gen_pos, write_by_hand:
  parameters controlling supercell search, commensurability scan and whether to dump a POSCAR.

Commensurate-angle scan:

• max_angle, min_angle: scan range in degrees
• lim: integer cutoff for commensurate search
• step: scan step (deg)
• accurate: numerical matching tolerance

Tight-Binding / Wannier tb

• theta_deg: twist angle in degrees
• cores: number of CPU cores for TB tasks
• isparse: whether to use sparse workflow (recommended for large systems)
• k_path: list of high-symmetry points in fractional coordinates
• nk: total number of k-points along the path
• labels: labels for each waypoint (e.g. ["G","M","K","G"])
• ylim: energy window for plotting, e.g. [-0.2, 0.2]

Wannier options (when struc.read_mode="model"):

• wannier.prefix: e.g. "wannier90" for inputs/wannier90.win and inputs/wannier90_hr.dat
• wannier.fermi_energy: Fermi level; if omitted, the program attempts to read inputs/FERMI_ENERGY, otherwise defaults to 0.0.

k·p section kp

• theta_deg: twist angle (deg)
• tr: truncation index for reciprocal shells (default ≈3)
• valley_pos: valley position in reduced coordinates, e.g. [2/3, 1/3]
• mono_lat: 2×2 real-space primitive lattice of the monolayer
• valley_name: "K" or "M" (controls mapping of the moiré reciprocal lattice)
• V_z / m_z: layer potential and mass term for simple 2×2 Dirac-like models

Coupling definition:

• inter_idxs / intra_idxs: lists of integer index pairs for moiré reciprocal vectors (e.g. [[0,0],[1,0],...])
• inter_coes: list of coupling matrices corresponding to inter_idxs, as:
  • real numbers,
  • complex numbers [re, im],
  • or 2D complex matrices (nested lists).
• intra_coes: [top_list, bottom_list], each element aligned with intra_idxs.
• k-path parameters: path, nk, labels, ylim (same as TB).

---

Complex numbers in JSON

• A bare scalar x is interpreted as a real number.
• [re, im] is interpreted as a complex number re + i·im.
• Complex matrices are written as nested lists of [re, im] entries.

---

Complete Example input.json

Below are shortened examples; you can adapt them to your own systems.

1. Geometry: scan commensurate angles – struc.find_com

{
  "mode": "struc",
  "task": "find_com",
  "io": { "input_dir": "./inputs", "output_dir": "./outputs", "fig_comm": "comm_angles.png" },
  "struc": {
    "read_mode": "POSCAR",
    "read_layer": "bilayer",
    "pos_path": "./inputs",
    "pos_prefix": "POSCAR",
    "ihetero": false,
    "d_AA": 3.5,
    "d_AB": 3.35,
    "max_angle": 5.0,
    "min_angle": 0.5,
    "lim": 12,
    "step": 0.02,
    "accurate": 0.01
  },
  "tb": { "theta_deg": 1.10 }
}

Output: outputs/commensurate_angle.txt and outputs/comm_angles.png.

2. Geometry: generate moiré supercell – struc.gen_pos

{
  "mode": "struc",
  "task": "gen_pos",
  "io": { "input_dir": "./inputs", "output_dir": "./outputs", "fig_moire": "moire_points.png" },
  "struc": {
    "read_mode": "POSCAR",
    "read_layer": "bilayer",
    "pos_path": "./inputs",
    "pos_prefix": "POSCAR",
    "ihetero": false,
    "d_AA": 3.5,
    "d_AB": 3.35,
    "zero_point": [0, 0],
    "lim": 20,
    "search": 0,
    "accurate": 0.01,
    "if_gen_pos": true
  },
  "tb": { "theta_deg": 1.10 }
}

Outputs: moiré point plot outputs/moire_points.png, and a generated/overwritten POSCAR at the project root.

3. TB: band structure from POSCAR – tb.band

{
  "mode": "tb",
  "task": "band",
  "io": {
    "input_dir": "./inputs",
    "output_dir": "./outputs",
    "fig_band": "tb_band.png",
    "show_plots": false
  },
  "struc": {
    "read_mode": "POSCAR",
    "read_layer": "bilayer",
    "pos_path": "./inputs",
    "pos_prefix": "POSCAR",
    "ihetero": false,
    "d_AA": 3.5,
    "d_AB": 3.35,
    "if_gen_pos": true
  },
  "tb": {
    "theta_deg": 1.10,
    "cores": 4,
    "isparse": false,
    "k_path": [[0,0],[0.5,0],[0.3333333,0.3333333],[0,0]],
    "nk": 201,
    "labels": ["G","M","K","G"],
    "ylim": [-0.2, 0.2]
  }
}

4. TB: band structure from Wannier90 – tb.band

{
  "mode": "tb",
  "task": "band",
  "io": { "input_dir": "./inputs", "output_dir": "./outputs", "fig_band": "tb_band_w90.png" },
  "struc": { "read_mode": "model", "ihetero": false },
  "tb": {
    "theta_deg": 1.10,
    "cores": 4,
    "isparse": false,
    "wannier": { "prefix": "wannier90", "fermi_energy": 0.0 },
    "k_path": [[0,0],[0.5,0],[0.3333333,0.3333333],[0,0]],
    "nk": 201,
    "labels": ["G","M","K","G"]
  }
}

If fermi_energy is not specified, the code tries to read inputs/FERMI_ENERGY; if missing, it defaults to 0.0.

5. k·p: band structure – kp.band

{
  "mode": "kp",
  "task": "band",
  "io": { "output_dir": "./outputs", "fig_band": "kp_band.png" },
  "kp": {
    "theta_deg": 1.10,
    "tr": 3,
    "valley_pos": [0.6666667, 0.3333333],
    "mono_lat": [[2.46, 0.0], [1.23, 2.13162820728]],
    "valley_name": "K",
    "V_z": 0.0,
    "m_z": 0.0,
    "inter_idxs": [[0,0],[1,0],[0,1]],
    "inter_coes": [[[0,0],[10,0]], [[0,0],[10,0]], [[0,0],[10,0]]],
    "intra_idxs": [[0,0]],
    "intra_coes": [
      [[[0,0],[0,0]], [[0,0],[0,0]]],
      [[[0,0],[0,0]], [[0,0],[0,0]]]
    ],
    "path": [[0,0],[0.5,0],[0.3333333,0.3333333],[0,0]],
    "nk": 201,
    "labels": ["G","M","K","G"],
    "ylim": [-0.2, 0.2]
  }
}

6. k·p: Chern number – kp.chern

{
  "mode": "kp",
  "task": "chern",
  "io": { "output_dir": "./outputs" },
  "kp": {
    "theta_deg": 1.10,
    "tr": 3,
    "valley_pos": [0.6666667, 0.3333333],
    "mono_lat": [[2.46, 0.0], [1.23, 2.13162820728]],
    "valley_name": "K",
    "V_z": 0.0,
    "m_z": 0.0,
    "inter_idxs": [[0,0]],
    "inter_coes": [[[0,0],[10,0]]],
    "intra_idxs": [],
    "intra_coes": [[],[]],
    "chern": { "n": 31, "band_index": 0 }
  }
}

Output: outputs/chern_number.txt (integer Chern number).

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Outputs & Visualization

• Band structures and k-path

  • band.txt: real matrix of shape (nk, nbands)
  • k_vec.txt: k-points in reduced coordinates
  • k_dist.txt: accumulated arc length along the path
  • k_node.txt: positions of high-symmetry points (x-axis ticks)
  • fig_band: band plot (PNG), name set via io.fig_band

• Geometry

  • commensurate_angle.txt: twist angle, number of commensurate solutions, and a proxy for supercell size
  • fig_moire: moiré point distribution plot
  • POSCAR: generated/overwritten when running struc.gen_pos

To display plots interactively, set io.show_plots=true, or open the PNG files directly from the outputs/ directory.

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Parallelism, Sparse Workflow & Large Systems

• tb.cores: number of CPU cores used in TB calculations.

Sparse workflow (recommended for large systems, with tb.isparse=true):

1. Use methods on TwistTB (in twist_tb.py) to build sparse blocks:
   • TwistTB.gen_r_ham_sparse_parallel(...): generate real-space sparse blocks
   • TwistTB.export_sparse_blocks(out_dir): export H0.npz, HRm.npz, and R_vectors.npy
2. At run time, assemble H(k) from these blocks via a helper such as build_ham_from_npz(k, out_dir) and/or compute low-energy eigenpairs using:
   • solver.solve_low_energy_streaming_parallel(...) (sparse, streaming).

Dense workflow:

• Build all dense Hamiltonians H(k) at once, e.g. gen_all_k_ham(k_vec)
• Diagonalize using solver.solve_all_parallel(...).

Windows note: wrap parallel entry points in

if __name__ == "__main__":
    main()

to be compatible with the spawn start method on Windows.

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FAQ (short)

• Q1: POSCAR vs. Wannier90?

  • If you already have high-quality Wannier parameters → use read_mode="model".
  • If you start from structural geometry or only need moiré geometry → use read_mode="POSCAR".

• Q2: Parallel runs crash on Windows?

  • Wrap calls in if __name__ == "__main__": as above.
  • Test with tb.cores = 1 first, then increase gradually.

• Q3: Band calculation is too slow / runs out of memory?

  • Reduce nk or simplify the high-symmetry path.
  • Use sparse workflow (tb.isparse=true) plus streaming solvers.
  • Check BLAS thread counts (the code tries to clamp them, but it’s still good practice to set reasonable values in the environment).

• Q4: How to write complex matrices in JSON?

  • Use [re, im] for each entry and nested lists for matrices (see “Complex numbers in JSON”).

• Q5: No figures appear?

  • Set io.show_plots=true, or open the PNG files in outputs/.

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Acknowledgements & Contributions

• Issues and pull requests are welcome for improving documentation, examples, and code quality.
• If this toolkit is useful for your research, a citation or acknowledgement is greatly appreciated.