pyscf-vorticity
Geometric correlation analysis for quantum chemistry
What it does
Computes an effective correlation dimension γ from first-principles two-particle reduced density matrices, helping you select the optimal DFT functional for your system.
Quick Start
from pyscf import gto, scf, fci
from pyscf_vorticity import compute_vorticity
# Your molecule
mol = gto.M(atom='...', basis='cc-pvdz')
mf = scf.RHF(mol).run()
cisolver = fci.FCI(mf)
E_fci, fcivec = cisolver.kernel()
# Compute vorticity
rdm2 = cisolver.make_rdm12(fcivec, mol.nao, nelec)[1]
V, k = compute_vorticity(rdm2, mol.nao)
alpha = abs(E_fci - mf.e_tot) / V
# → Use γ to guide functional selection
HOW TO: Choosing Your Functional
The following table provides practical guidelines based on physical interpretation of γ. These are recommendations, not absolute rules.
| Your γ | Optimal a | Recommended Functional |
|---|---|---|
| γ ≈ 0 | a ≈ 1.0 | HF or high exact exchange hybrid |
| γ ≈ 1 | a ≈ 0.5 | M06-2X, BH&HLYP |
| γ ≈ 2 | a ≈ 0.33 | PBE0, B3LYP |
| γ ≈ 3-4 | a ≈ 0.25 | B3LYP |
| γ > 5 | a < 0.15 | TPSSh, pure GGA |
Guideline formula: a ≈ 1 / (1 + γ)
WHY: The Physics Behind It
The Problem
Standard DFT functionals contain empirical "magic numbers" - like B3LYP's 20% exact exchange - that work on average but fail systematically for:
- Strong correlation (Mott insulators, transition metals)
- Static correlation (bond breaking, diradicals)
- Weak correlation (metals)
The Root Cause
Most functionals implicitly assume a fixed correlation structure.
Reality: correlation strength varies across systems.
The Solution
The exchange-correlation energy scales with a vorticity measure V, allowing extraction of the correlation exponent γ through system-size dependence.
γ characterizes how correlated your system is:
| γ value | Correlation type | Electron behavior |
|---|---|---|
| γ → 0 | Strong (Mott) | Localized, atomic-like |
| γ ≈ 1 | Intermediate | Partially localized |
| γ ≈ 2 | Moderate | Molecular bonding |
| γ > 3 | Weak (metallic) | Fully delocalized |
HOW TO: Strong Correlation Systems
When γ → 0, standard DFT typically fails.
What to do:
- Use high exact exchange
mf = dft.RKS(mol)
mf.xc = 'HF*0.5 + PBE*0.5' # 50% exact exchange
- Consider range-separated hybrids
mf.xc = 'camb3lyp'
- Or go beyond DFT (CASSCF, DMFT, CC)
WHY this works:
Strong correlation (γ → 0) indicates localized electrons. Localized electrons require exact exchange to avoid self-interaction error. GGA/LDA assume delocalized electrons → systematic failure.
HOW TO: Weak Correlation (Metals)
When γ > 3:
mf.xc = 'PBE' # Pure GGA
mf.xc = 'TPSS' # Meta-GGA
WHY:
Delocalized electrons screen effectively. Exact exchange over-localizes → wrong band structure.
Validated Examples
H₂ Dissociation
R = 0.74Å: α = 0.062 (equilibrium)
R = 1.50Å: α = 0.039 (minimum)
R = 5.00Å: α = 0.097 (dissociated)
U-shaped α(R) captures weak → strong correlation transition.
Atoms
He (2e): α = 0.090
Be (4e): α = 0.003
Ne (10e): α = 0.004
The 30× drop from 2e to 4+e systems ("α-cliff") reflects the emergence of many-body correlation structure.
Installation
pip install git+https://github.com/miosync-masa/pyscf-vorticity.git
GPU acceleration (recommended):
pip install jax jaxlib
Citation
@article{iizumi2025geometric,
title={Geometric Origin of Exchange-Correlation in Density Functional Theory},
author={Iizumi, Masamichi},
journal={arXiv preprint},
year={2025}
}
License
MIT
Metadata
Release files for pyscf-vorticity 0.1.0
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