occpy 0.9.1

A library for quantum chemistry

Open Computational Chemistry (OCC)

A next-generation quantum chemistry and crystallography program and library, designed for modern computational workflows.

Note: OCC is in active development and undergoes frequent changes. The API and features are not yet stable.

Installation

From PyPI

The easiest way to install OCC is via pip:

pip install occpy

Supported Python versions:

• Python 3.10, 3.11, 3.12, 3.13

Pre-built wheels are available for:

• Linux (x86_64)
• macOS (x86_64 and ARM64/Apple Silicon via universal2 wheels)

Features

Quantum Chemistry

OCC provides comprehensive functionality for ground-state single-point calculations:

• Electronic Structure Methods

  • Hartree-Fock (Restricted, Unrestricted, and General Spinorbitals)
  • Density-Functional Theory (Restricted & Unrestricted)
    • Supported approximations: LDA, GGA, meta-GGA
    • Global hybrid functionals (range-separated support planned)
  • Density fitting (RI-JK) with auxiliary basis sets
  • Implicit solvation via SMD
  • XDM dispersion model

• Property Calculations

  • Molecular and atomic multipole moments (up to hexadecapole)
  • Electron density, Electrostatic potential
  • CHELPG charges
  • Isosurfaces, generation of volumetric data and more...

Crystal Structure Analysis

• CIF file processing (via gemmi)
• Advanced periodic analysis:
  • Fast periodic bond detection
  • Symmetry-unique molecule generation
  • Dimer identification
• Energy calculations:
  • CrystalExplorer model energies
  • Automatic direct space lattice energy summation
  • Wolf summation for neutral molecular crystals
• Surface analysis:
  • Hirshfeld surfaces
  • Promolecule surfaces

Additional Features

• Spherical harmonic transforms (FFT-based)
• Molecular point group detection
• File format support:
  • Gaussian fchk files (read/write)
  • Molden files
  • NumPy .npy arrays (write)
  • QCSchema JSON
  • Basic Gaussian input files
• Geometric algorithms:
  • Marching cubes
  • Morton codes for linear-hashed octrees
• Electronegativity equilibration method for charges
• Python bindings via nanobind

Python API Examples

import occpy
from occpy import Crystal, Molecule, AOBasis, HartreeFock, DFT
from occpy import SpinorbitalKind

# Set up basic configuration
occpy.set_log_level(occpy.LogLevel.WARN)  # Configure logging level
# occpy.set_data_directory("/path/to/basis/sets")  # Optional: Set basis set path

# Load molecule from XYZ file
mol = Molecule.from_xyz_file("h2o.xyz")

# Basic Restricted Hartree-Fock calculation
basis = AOBasis.load(mol.atoms(), "6-31G")
hf = HartreeFock(basis)
scf = hf.scf()
energy = scf.run()
wfn = scf.wavefunction()

# DFT calculation
dft = DFT("B3LYP", basis)
ks = dft.scf(SpinorbitalKind.Unrestricted)
ks.set_charge_multiplicity(0, 1)
energy = ks.run()

# Crystal structure analysis
crystal = Crystal.from_cif_file("structure.cif")
dimers = crystal.symmetry_unique_dimers(10.0)  # Get unique dimers within 10 Å

For more examples and detailed API documentation, please refer to the documentation.

Build from Source

Prerequisites

• C++17 compliant compiler (GCC 10+ recommended)
• CMake 3.15+
• Ninja (recommended) or Make

Dependencies

OCC uses modern C++ libraries to provide its functionality:

Library	Version	Description
CLI11	2.4.2	Command line parser
Eigen3	3.4.0+	Linear algebra
fmt	11.0.2	String formatting
gemmi	0.6.5	Crystallographic file handling
LBFGS++	master	Optimization algorithms
libcint	6.1.2	Gaussian integrals
libxc	6.2.2	Exchange-correlation functionals
nlohmann/json	3.11.3	JSON handling
scnlib	4.0.1	String parsing
spdlog	1.15.0	Logging
unordered_dense	4.5.0	Hash containers

Optional dependencies:

• nanobind (2.4.0) - For Python bindings

Most dependencies are automatically handled through CPM. System-installed versions of Eigen3 and libxc can be used if available.

OCC's geometry optimization implementation for molecules made significant use of the code and documentation from pyberny - the files in the opt submodule are therefore subject to the MPL license.

Build Instructions

1. Clone the repository:

   git clone https://github.com/peterspackman/occ.git
   cd occ
   

2. Configure dependency caching (recommended):

   export CPM_SOURCE_CACHE="$HOME/.cache/cpm"
   

3. Build with CMake:

   mkdir build && cd build
   
   # Using system dependencies (if available)
   cmake .. -GNinja
   
   # OR download all dependencies
   cmake .. -GNinja -DUSE_SYSTEM_LIBXC=OFF -DUSE_SYSTEM_EIGEN=OFF
   
   # Build the executable
   ninja occ
   

CMake Options

• USE_SYSTEM_LIBXC: Use system-installed libxc (default: ON)
• USE_SYSTEM_EIGEN: Use system-installed Eigen3 (default: ON)
• WITH_PYTHON_BINDINGS: Build Python bindings (default: OFF)
• USE_MLX: Enable MLX integration (default: OFF)
• USE_QCINT: Use QCInt instead of libcint (default: OFF)
• ENABLE_HOST_OPT: Enable host-specific optimizations (default: OFF)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Citation

If you use OCC in your research, please cite the appropriate papers for all functionals, methods etc. you use, along with the citations for the core dependencies here.