Poraquê
Poraquê is a research code for electronic structure and machine learning on 3D scalar fields (external potential, charge density, kinetic energy density).
Status
| Area | State |
|---|---|
poraque.fields |
3D scalar fields (EXTCAR/CHGCAR/TAUCAR) on one shared grid |
poraque.fields.io |
Pluggable ingestion — VASP working; Quantum ESPRESSO and GPAW scaffolded |
poraque.ml |
Fourier Neural Operator pipeline, handles per-material grid shapes |
| Everything else | Legacy; being reorganized |
Scripts
# Validate the external-potential model against reference VASP EXTCAR files
python scripts/validate_vasp_data.py --fit-sigma --form-factor
# Train the two neural operators (EXTCAR -> CHGCAR, CHGCAR -> TAUCAR)
python scripts/train_fno.py --resolution 32 --epochs 200
Notes
plan/pi_fno.md— roadmap for the physics-informed operator (PI-FNO).plan/fno_physics.md— how KS-DFT and OF-DFT relate to what the two models learn.
Installation
git clone https://github.com/seixas-research/poraque.git
cd poraque
pip install -e .
License
Open source under the MIT License.
1 maintainer
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