# Seminumerical exchange (SGX)#

*Modules*: `pyscf.sgx`

The SGX module implements seminumerical computation of the exchange matrix.

## Introduction#

Direct computation of the Hartree-Fock exchange matrix in the atomic orbital basis scales poorly with system size. To achieve better scaling, one three-dimensional integral in the 6-dimensional two-electron integrals can be computed analytically, while the other can be evaluated on a real-space grid, as proposed by Friesner [80]. The PySCF implementation resembles the chain-of-spheres (COSX) algorithm of Neese et al. [81], but uses more conservative grids and a slightly different P-junction screening function. Overlap fitting is used to reduce aliasing errors [82]. SGX scales as \(O(N^2)\) with system size, as opposed to the \(O(N^4)\) scaling of analytical exchange [81].

## Usage and Example#

Any `scf.hf.SCF`

object `mf`

can be converted to an equivalent object that computes the Coulomb and exchange matrices with SGX instead of analytical integration by calling `sgx.sgx_fit(mf)`

.

```
#!/usr/bin/env python
'''
This example shows how to use pseudo spectral integrals in SCF calculation.
'''
from pyscf import gto
from pyscf import scf
from pyscf import sgx
mol = gto.M(
atom='''O 0. 0. 0.
H 0. -0.757 0.587
H 0. 0.757 0.587
''',
basis = 'ccpvdz',
)
# Direct K matrix for comparison
mf = scf.RHF(mol)
mf.kernel()
# Using SGX for J-matrix and K-matrix, without using P-junction screening
mf = sgx.sgx_fit(scf.RHF(mol), pjs=False)
mf.kernel()
# Using RI for Coulomb matrix while K-matrix is constructed with SGX method
mf.with_df.dfj = True
mf.kernel()
# Turn on P-junction screening to accelerate large calculations
# (uses algorithm similar to COSX)
mf.with_df.pjs = True
mf.kernel()
# direct_scf_sgx turns on direct SCF for SGX
# JK matrix is rebuilt from scratch every rebuild_nsteps steps
mf.direct_scf_sgx = True
# If grids_level_i == grids_level_j, no grid switch occurs
mf.with_df.grids_level_i = 1
mf.kernel()
# If dfj is off at runtime, it is turned on and a user warning is issued
# because SGX-J cannot be used with P-junction screening.
mf.with_df.dfj = False
mf.kernel()
# Use direct J-matrix evaluation (slow, primarily for testing purposes)
mf = sgx.sgx_fit(scf.RHF(mol), pjs=False)
mf.with_df.direct_j = True
mf.with_df.dfj = False
mf.kernel()
```

In this case, the error of DFJ+SGX compared to analytical exchange is about 0.03 mEh. The line

```
mf.with_df.dfj = True
```

specifies to compute the Coulomb matrix using density fitting (DF-J) while using SGX for the exchange matrix.

The `pjs`

option turns on P-junction screening, which means that the three-center integrals are screened by the density matrix.
If a three-center integral gets contracted only with negligibly small density matrix elements, it is not computed.
This feature can only be used with `dfj=True`

.
If `dfj=False`

and `pjs=True`

, `dfj`

is set to `True`

, and a warning is issued.
The P-junction screening threshold is determined by `mf.direct_scf_tol`

.

Direct evaluation of the J matrix can be used by setting `mf.with_df.direct_j = True`

, but this is much slower than SGX-J or DF-J and only intended for testing purposes.

## Adjustable Parameters#

Calling the `sgx_fit`

function on an `scf.hf.SCF`

object returns an equivalent `SGXHF`

object with a `with_df`

attribute that handles SGX integration. To use a non-default auxiliary basis (for `dfj=True`

), `auxbasis`

can be specified in the call to `sgx_fit`

. In addition, there are various adjustable parameters for SGX:

`with_df`

attributes:

`grids_level_i`

: The grid level to use for initial SCF iterations.`grids_level_f`

: The grid level to use for final SCF iterations.`grids_thrd`

: The grid points where no atomic orbital is significant (has a value greater than this threshold) are removed from consideration.`grids_switch_thrd`

: The threshold for the magnitude of the change in the density matrix that triggers the switch from the initial grid specified by`grids_level_i`

to the final grid specified by`grids_level_f`

.`blockdim`

: The maximum number of grid points to loop over at once. The number of grid points per batch is the minimum of`blockdim`

and the maximum number of points allowed by the memory available for the calculation. The maximum memory can be adjusted by setting the`max_memory`

attribute, which is initially set to`mol.max_memory`

, the max memory setting for the Mole object.`dfj`

: If`True`

, density fitting is used for the J-matrix. If`False`

, SGX is used.`direct_j`

: If`True`

, direct evaluation of the J-matrix is used (slow, for testing only).`pjs`

: If`True`

, P-junction screening is used. Threshold determined by`direct_scf_tol`

.

`SGXHF`

attribute:

`direct_scf_sgx`

: Whether to use direct SCF within the SGX module, meaning that the J and K matrices are evaluated from the difference in the density matrix from the previous iteration.`rebuild_nsteps`

: Rebuild the SGX JK matrix from scratch every`rebuild_nsteps`

steps (default 5). Set to 0 to turn off rebuilding the JK matrix (Warning: This can cause creeping numerical error).