q2mm 5.0.0a1

Quantum-guided molecular mechanics force field optimization

Q2MM

Quantum-guided molecular mechanics force field optimization.

Q2MM optimizes molecular mechanics (MM) force field parameters by minimizing the difference between MM-calculated properties and quantum mechanics (QM) reference data. It is designed for building transition state force fields (TSFFs) that enable rapid virtual screening of enantioselective catalysts.

📖 Documentation

Why Q2MM?

• Hessian-informed initialization — the Seminario method extracts bond and angle force constants directly from QM Hessians, providing excellent starting parameters before optimization begins.
• Open-source backends — first-class support for OpenMM and Psi4 alongside commercial packages (Gaussian, Schrödinger, Tinker).
• Clean, modular architecture — format-agnostic data models (ForceField, Q2MMMolecule) decouple algorithms from file formats.
• Modern optimization — powered by scipy.optimize with L-BFGS-B, Nelder-Mead, trust-region, and Levenberg-Marquardt methods.
• Transition state support — negative force constants, torsion parameters, and proper eigenvalue handling for saddle-point geometries.

Quick Start

pip install -e .

import numpy as np
from q2mm.models.molecule import Q2MMMolecule
from q2mm.models.seminario import estimate_force_constants

# Load QM data (coordinates + Hessian from your QM package)
mol = Q2MMMolecule.from_xyz("ts-optimized.xyz")
mol.hessian = np.load("ts-hessian.npy")  # Hartree/Bohr²

# Estimate force constants from the QM Hessian (Seminario method)
ff = estimate_force_constants(mol, au_hessian=True)

print(f"Bonds: {len(ff.bonds)}, Angles: {len(ff.angles)}")
for b in ff.bonds:
    print(f"  {b.elements}: k={b.force_constant:.3f} mdyn/Å")

See the Tutorial for a complete end-to-end workflow.

Supported Backends

Backend	Type	License
OpenMM	MM	MIT
Psi4	QM	BSD-3
Tinker	MM	Free (academic)
Gaussian	QM	Commercial
Schrödinger	QM/MM	Commercial

License

BSD-3-Clause. See LICENSE.