Loofah-SC-Percolation-Sim

Simulation-first reference implementation for percolation-controlled superconducting transport on loofah-templated biomorphic carbon architectures with conformal superconducting coatings.

This repository supports the 2026 simulation-first manuscript:

Antonios Valamontes (2026)
Loofah-Templated Biomorphic Carbon Superconducting Composites: A Simulation-Driven Platform for Percolation-Controlled Transport Predictions.

What this repo contains

• Geometry: stochastic, loofah-like hierarchical network generator (src/loofah_sc_sim/geometry.py)
• Coating continuity & defects: binary superconducting state model (src/loofah_sc_sim/coating.py)
• Percolation: spanning-cluster detection (src/loofah_sc_sim/percolation.py)
• Transport proxy: bottleneck-constrained critical current estimator (src/loofah_sc_sim/transport.py)
• Reproducible outputs: JSON summaries embedding full configuration (src/loofah_sc_sim/io.py)
• Notebooks: Colab-ready notebooks reproducing the numerical workflow (notebooks/)
• Tests: lightweight unit tests for percolation and transport logic (tests/)

The workflow schematic in the manuscript is produced in LaTeX (TikZ); numerical sweeps and summaries are produced here.

Quick start (Google Colab)

1. Open notebooks/00_Colab_Setup.ipynb
2. Run notebooks in order:

• 00_Colab_Setup.ipynb
• 01_Scaffold_Generation.ipynb
• 02_Coating_and_Percolation.ipynb
• 03_Transport_and_Ic.ipynb
• 04_Parameter_Sweep.ipynb

This reproduces the main numerical outputs and generates JSON summaries under results/.

Local install (recommended)

Option A: conda (environment.yml)

conda env create -f environment.yml
conda activate loofah-sc-sim
python -m pip install -e .

Option B: pip

python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -e .

Reproducing the paper outputs

Canonical configurations

Reference configurations are provided under configs/:

• configs/paper_small_fast.json (fast smoke test)
• configs/paper_nominal.json (nominal)
• configs/sweep_recipe.json (parameter sweep recipe)

Command-line sweep (produces sweep.json)

python scripts/run_sweep.py --out results/sweep.json --n-realizations 100 --seed 42

Single case (sanity check)

python scripts/make_one_case.py --out results/one_case.json --seed 42

Determinism and randomness

All runs are deterministic under fixed random seeds. See docs/RANDOMNESS.md for the seed policy and reproducibility expectations.

Figure ↔ code mapping

See docs/FIGURE_MAP.md for a direct mapping between paper artifacts and the repository.

Repository layout

• src/loofah_sc_sim/ – core library
• scripts/ – CLI helpers (single case, sweep)
• notebooks/ – Colab-ready reproduction notebooks
• configs/ – canonical configurations used for reproduction
• results/ – generated outputs (JSON summaries)
• docs/ – reproducibility notes

License

MIT (see LICENSE).

Citation

If you use this repository, please cite the associated manuscript and/or the repository archive. A CITATION.cff file is included for GitHub citation support.