jax-hf 1.0.2

A package that provides a jax-based Hartree-Fock optimization solver for simple continuum models.

jax_hf — JAX Hartree–Fock on k‑grids

jax_hf provides a JAX implementation of a Hartree–Fock self‑consistent field (SCF) loop on uniform 2D k‑grids, with optional JIT compilation.

• FFT‑based exchange in k‑space
• Dense Hermitian diagonalization (JAX eigh)
• DIIS/EDIIS‑style mixing (robust convergence)
• NumPy/JAX‑friendly API, easy to integrate with other JAX code

Project links:

• PyPI: https://pypi.org/project/jax-hf/
• Source: https://github.com/skilledwolf/jax_hf

Installation

Users (PyPI):

pip install jax-hf

Note: jax_hf depends on JAX. For CPU‑only installs, pip will usually pull a working wheel automatically. For GPU, follow JAX’s official install guide to select the correct extras/wheels for your CUDA/cuDNN stack.

Developers (editable install):

git clone https://github.com/skilledwolf/jax_hf.git
cd jax_hf
pip install -e .

Quick start

import numpy as np
import jax.numpy as jnp
import jax_hf

# Grid and shapes
nk = 128; d = 2
weights = np.ones((nk, nk)) * ((2/nk)*(2/nk) / (2*np.pi)**2)  # scalar mesh measure
H = np.zeros((nk, nk, d, d), dtype=np.complex128)
K = np.linspace(-1.0, 1.0, nk)
Vq = (1.0 / np.sqrt((K[:, None]**2 + K[None, :]**2) + 0.1)).astype(np.complex128)[..., None, None]
P0 = np.zeros_like(H)

# Target electron density (half‑filling)
ne_target = 0.5 * d * weights.sum()

# Build HF kernel (JAX arrays inside)
kernel = jax_hf.HartreeFockKernel(
    weights,                 # (nk, nk)
    H,                       # (nk, nk, d, d)
    Vq,                      # (nk, nk, 1, 1) or (nk, nk, d, d)
    T=0.5,                   # temperature
)

# JIT‑compile the SCF iteration function (optional but recommended)
hf_iter = jax_hf.jit_hartreefock_iteration(kernel)

P_fin, F_fin, E_fin, mu_fin, n_iter, history = hf_iter(
    P0, float(ne_target),
    max_iter=50, comm_tol=1e-3, diis_size=6, log_every=None,
)
print("iters:", int(n_iter), "mu:", float(mu_fin), "E:", float(E_fin))

API

class HartreeFockKernel:
    def __init__(self, weights, hamiltonian, coulomb_q, T: float):
        ...

def hartreefock_iteration(
    P0, electrondensity0, hf_step: HartreeFockKernel,
    *, max_iter=100, comm_tol=5e-3, diis_size=4, log_every: int | None = 1,
):
    """Runs SCF and returns (P_fin, F_fin, E_fin, mu_fin, n_iter, history)."""

def jit_hartreefock_iteration(hf_step: HartreeFockKernel):
    """Returns a jitted version of hartreefock_iteration with static args."""

• shapes: weights is (nk, nk), hamiltonian is (nk, nk, d, d), coulomb_q is (nk, nk, 1, 1) or (nk, nk, d, d), P0 matches (nk, nk, d, d).
• returns: converged density P_fin, mean‑field F_fin, total energy, chemical potential mu_fin, iteration count, and a small history dict with energy/commutator traces.

Versioning

Versions are derived from git tags using setuptools_scm. Tags like v1.2.3 produce version 1.2.3; non‑tag builds produce development versions.

License

BSD 2‑Clause — see LICENSE.

Third‑party notices: see THIRD_PARTY_NOTICES.md.

Author: Dr. Tobias Wolf