pyfock 0.1.2

A simplistic and efficient pure-python quantum chemistry library from Phys Whiz.

PyFock

PyFock

A pure Python Gaussian basis DFT code with GPU acceleration for efficient quantum chemistry calculations
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Table of Contents

1. About The Project
2. Key Features
3. Installation
4. Quick Start
5. Usage
6. Graphical User Interface
7. Tutorials
8. Documentation
9. Roadmap
10. Contributing
11. License
12. Citation
13. Contact
14. Acknowledgments

About The Project

PyFock is a pure Python quantum chemistry package that enables efficient Kohn-Sham density functional theory (DFT) calculations for molecular systems. Unlike traditional quantum chemistry codes written in Fortran or C/C++, PyFock is written entirely in Python—including all performance-critical molecular integral evaluations—while achieving computational efficiency comparable to established codes like PySCF and Psi4.

What Makes PyFock Different?

• 100% Pure Python: All code, including computationally intensive molecular integrals, is written in Python
• High Performance: Achieves efficiency comparable to C/C++ backends through Numba JIT compilation, NumPy, NumExpr, SciPy, and CuPy
• GPU Acceleration: Leverages CUDA via Numba and CuPy for up to 14× speedup on large systems
• Easy Installation: Simple pip install on all major operating systems (Linux, macOS, Windows)
• Accessible: Designed for education, prototyping, and community development
• Near-Quadratic Scaling: ~O(N²·⁰⁵) scaling through density fitting with Cauchy-Schwarz screening
• Gaussian-Type Orbitals: Employs GTOs as basis functions for molecular calculations
• Efficient Parallelization: Multi-core CPU support and multi-GPU acceleration capabilities

Performance Highlights

• Numerical accuracy: Consistent with PySCF (< 10⁻⁷ Ha)
• Parallel efficiency: Comparable to state-of-the-art C++ backends on multicore CPUs
• GPU speedup: Up to 14× faster than 4-core CPU execution for large systems
• Scaling: Near-quadratic ~O(N²·⁰⁵) for electron repulsion integrals (Coulomb term)
• XC evaluation: Sub-quadratic scaling ~O(N¹·²⁵⁻¹·⁵) for exchange-correlation contributions

Key Features

• ✅ Pure Python Implementation: Including molecular integral evaluations (overlap, kinetic, nuclear attraction, electron repulsion integrals)
• ✅ Density Fitting: Efficient density fitting approximation with Cauchy-Schwarz screening
• ✅ GPU Acceleration: Full GPU support for integral evaluation, XC term, and matrix operations
• ✅ Multiple Integration Schemes:
  • Classical Taketa-Huzinaga-O-ohata scheme
  • Rys quadrature method (roots 1–10) for efficient ERI evaluation
• ✅ XC Functionals: Support for LDA and GGA functionals via LibXC integration
• ✅ DIIS Convergence: Direct inversion of iterative subspace for SCF acceleration
• ✅ Parallel Execution: Multi-core CPU and multi-GPU support via Numba and Joblib
• ✅ Modular Design: Standalone integral modules for benchmarking and embedding
• ✅ Web-based GUI: Interactive interface for visualization and input generation
• ✅ Cartesian and Spherical Basis: Support for both CAO and SAO representations
• ✅ Cross-Platform: Works on Linux, macOS, and Windows

Installation

Basic Installation

PyFock can be easily installed via pip:

pip install pyfock

Installing from GitHub (Latest Development Version)

To get the latest development version directly from GitHub:

pip install git+https://github.com/manassharma07/pyfock.git

Or clone the repository and install locally:

git clone https://github.com/manassharma07/pyfock.git
cd pyfock
pip install -e .

Installing LibXC (Required Dependency)

PyFock requires LibXC for exchange-correlation functionals. The installation method depends on your system:

Using Conda (Recommended - Easiest Method)

conda install -c conda-forge pylibxc -y

On Ubuntu/Debian

sudo apt-get install libxc-dev
pip install pylibxc2

On macOS

brew install libxc
pip install pylibxc2

Note: The conda method is recommended as it works reliably across all platforms.

Optional Dependencies

For GPU acceleration:

pip install cupy-cuda11x  # Replace 11x with your CUDA version

Quick Start

Here's a minimal example to get you started with PyFock:

from pyfock import Basis, Mol, DFT

# Define molecule from XYZ file
mol = Mol(coordfile='h2o.xyz')

# Set up basis sets
basis = Basis(mol, {'all': Basis.load(mol=mol, basis_name='def2-SVP')})
auxbasis = Basis(mol, {'all': Basis.load(mol=mol, basis_name='def2-universal-jfit')})

# Configure DFT calculation (PBE functional)
funcx = 101  # PBE exchange (LibXC code)
funcc = 130  # PBE correlation (LibXC code)
dftObj = DFT(mol, basis, auxbasis, xc=[funcx, funcc])

# Set calculation parameters
dftObj.conv_crit = 1e-7
dftObj.max_itr = 20
dftObj.ncores = 4

# Run SCF calculation
energy, dmat = dftObj.scf()
print(f"Total Energy: {energy} Ha")

Usage

Computing Molecular Integrals

PyFock provides standalone access to all molecular integrals:

from pyfock import Integrals, Basis, Mol

mol = Mol(coordfile='h2o.xyz')
basis = Basis(mol, {'all': Basis.load(mol=mol, basis_name='def2-SVP')})

# One-electron integrals
S_ovlp = Integrals.overlap_mat_symm(basis)
V_kin = Integrals.kin_mat_symm(basis)
V_nuc = Integrals.nuc_mat_symm(basis, mol)

# Two-electron integrals (classical scheme)
ERI_slow = Integrals.conv_4c2e_symm(basis)

# Two-electron integrals (Rys quadrature - faster)
ERI_fast = Integrals.rys_4c2e_symm(basis)

# Three-center integrals for density fitting
ERI_3c2e = Integrals.rys_3c2e_symm(basis)

# Two-center integrals
ERI_2c2e = Integrals.rys_2c2e_symm(basis)

GPU-Accelerated Integrals

# GPU versions (returns CuPy arrays in device memory)
S_ovlp_gpu = Integrals.overlap_mat_symm_cupy(basis)
V_kin_gpu = Integrals.kin_mat_symm_cupy(basis)
V_nuc_gpu = Integrals.nuc_mat_symm_cupy(basis, mol)
ERI_3c2e_gpu = Integrals.rys_3c2e_symm_cupy(basis)

Converting Between Cartesian and Spherical Basis

# Convert from Cartesian to Spherical atomic orbitals
V_kin_CAO = Integrals.kin_mat_symm(basis)
c2sph_mat = basis.cart2sph_basis()
V_kin_SAO = np.dot(c2sph_mat, np.dot(V_kin_CAO, c2sph_mat.T))

Subset Evaluation

# Evaluate integrals for a subset of basis functions
S_ovlp_subset = Integrals.overlap_mat_symm(basis, slice=[0, 5, 0, 5])
# slice = [row_start, row_end, col_start, col_end]

Full DFT Calculation Example

from pyfock import Basis, Mol, DFT

# Initialize molecule
xyzFilename = 'benzene.xyz'
mol = Mol(coordfile=xyzFilename)

# Set up basis sets
basis_set_name = 'def2-SVP'
auxbasis_name = 'def2-universal-jfit'
basis = Basis(mol, {'all': Basis.load(mol=mol, basis_name=basis_set_name)})
auxbasis = Basis(mol, {'all': Basis.load(mol=mol, basis_name=auxbasis_name)})

# Configure XC functional (PBE)
funcx = 101  # Exchange
funcc = 130  # Correlation
funcidcrysx = [funcx, funcc]

# Initialize DFT object
dftObj = DFT(mol, basis, auxbasis, xc=funcidcrysx)

# Configure convergence and parallelization
dftObj.conv_crit = 1e-7
dftObj.max_itr = 20
dftObj.ncores = 4

# Run calculation
energyCrysX, dmat = dftObj.scf()
print(f"SCF Energy: {energyCrysX} Ha")

Generating Visualization Files

from pyfock import Utils

# Generate cube files for molecular orbitals and density
Utils.write_density_cube(dftObj, filename='benzene_density.cube')

Graphical User Interface

PyFock includes a web-based GUI for interactive calculations and visualization:

🌐 Try it online: https://pyfock-gui.bragitoff.com

GUI Features

• Interactive 3D Visualization: View molecules and molecular orbitals using Py3Dmol
• Easy Configuration: Select basis sets, functionals, and calculation parameters
• Automatic Cube File Generation: HOMO, LUMO, and density visualizations
• Input Script Generator: Export Python code for local execution
• PySCF Validation: Built-in comparison with PySCF for accuracy verification
• Molecule Library: Pre-loaded common molecules or custom XYZ input

Running GUI Locally

The GUI source code is available on GitHub and can be run locally:

git clone https://github.com/manassharma07/PyFock-GUI.git
cd PyFock-GUI
pip install -r requirements.txt
streamlit run app.py

Tutorials

Interactive Jupyter Notebooks

🚀 Coming Soon: Interactive tutorials on Kaggle and Google Colab

• Kaggle Notebook: Introduction to PyFock
• Google Colab: GPU-Accelerated DFT with PyFock
• Kaggle Notebook: Molecular Orbital Visualization
• Google Colab: Advanced Features and Benchmarking

Documentation

📚 Full Documentation: https://pyfock-docs.bragitoff.com

• Getting Started Guide
• API Reference
• Theory and Implementation
• Examples and Tutorials

Roadmap

• Density Fitting with Cauchy-Schwarz screening
• GPU acceleration for integrals and XC evaluation
• DIIS convergence acceleration
• Web-based GUI
• Rys quadrature (roots 1–10)
• Analytical gradients (in progress)
• Electron dynamics & Excited state calculations (RT-TDDFT)
• Periodic boundary conditions
• Hybrid functionals with exact exchange
• Multi-GPU parallelization
• Basis set optimization tools

See the open issues for a full list of proposed features and known issues.

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make PyFock better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Don't forget to give the project a star! ⭐ Thanks!

License

Distributed under the MIT License. See LICENSE file for more information.

Citation

If you use PyFock in your research, please cite:

@article{sharma2025pyfock,
  title={PyFock: A Pure Python Gaussian Basis DFT Code for CPU and GPU},
  author={Sharma, Manas},
  journal={[Journal Name]},
  year={2025},
  note={Manuscript in preparation}
}

PyPI Package: https://pypi.org/project/pyfock/

Contact

Manas Sharma

• Email: [e-mail]
• Website: manas.bragitoff.com
• LinkedIn: linkedin.com/in/manassharma07
• Project Homepage: https://pyfock.bragitoff.com
• Project Link: https://github.com/manassharma07/pyfock

Acknowledgments

• Prof. Dr. Marek Sierka and Friedrich Schiller University Jena for HPC access
• The open-source quantum chemistry community

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