Range separated functionals (RSF)¶

Introduction¶

Range separated functionals (RSF) are a subgroup of hybrid functionals. While conventional (global) hybrid functionals like PBE0 or B3LYP use fixed fractions of Hartree-Fock (HFT, E_XX) and DFT (E_X) exchange for exchange, f.e. 1/4 E_XX and 3/4 E_X in the case of PBE0, RSFs mix the two contributions by the spatial distance between two points, \(r_{12}\), using a soft function \(\omega_\mathrm{RSF}(\gamma, r_{12})\).

To achieve this, the coulomb interaction kernel, \(\frac{1}{r_{12}} = \frac{1}{|r_1 - r_2|}\), which appears in the exchange integral from HFT is split into two parts:

\(\frac{1}{r_{12}} = \underbrace{\frac{1 - [\alpha + \beta ( 1 - \omega_\mathrm{RSF} (\gamma, r_{12}))]}{r_{12}}}_{\text{SR, DFT}} + \underbrace{\frac{\alpha + \beta ( 1 - \omega_\mathrm{RSF} (\gamma, r_{12}))}{r_{12}}}_{\text{LR, HFT}}\),

the short-range (SR) part is handled by the exchange from a (semi-)local LDA or GGA functional such as PBE, while the long-range part (LR) is handled by the exchange from HFT. \(\alpha\) and \(\beta\) are functional dependent mixing parameters. \(\alpha \ne 0\) and \(\beta = 0\) resembles conventional global hybrids. RSFs with \(\alpha = 0\) and \(\beta \ne 0\) are usually denoted by LC and the name of the semi-local functional, f.e. LC-PBE. RSFs with \(\alpha \ne 0\) and \(\beta \ne 0\) are usually denoted by CAM and the name of the semi-local functional, f.e. CAM-BLYP.

For the separating function \(\omega_\mathrm{RSF}\), two functions are in common use: either the complementary error function, \(\omega_\mathrm{RSF} = \mathrm{erfc}(\gamma r_{12})\), or the Slater-function, \(\omega_\mathrm{RSF} = e^{(-\gamma r_{12})}\). While the use of the complementary error function is computationally fortunate for codes utilizing Gaussian type basis sets, the Slater-function give superior results in the calculation of Rydberg-state and charge transfer excitations. To distinguish between these both functions, functionals using the Slater-function append the letter “Y” to the RSF marker, f.e. LCY-PBE or CAMY-B3LYP, while functionals using the complementary error function keep the marker as it is, f.e. LC-PBE or CAM-B3LYP.

Besides \(r_{12}\), the separation functions use a second parameter, the screening factor \(\gamma\). The optional value for \(\gamma\) is under discussion. A density dependence is stated. For most RSF standard values for \(\gamma\) are defined, although it is possible to tune \(\gamma\) to optimal values for calculations investigating ionization potentials, charge transfer excitations and the binding curves of bi-radical cations.

Implementation¶

The implementation of RSFs in gpaw consists of two parts:

once the implementation of the semi-local functional part. This is done in libxc.
once the implementation of the Hartree-Fock exchange. This is done in hybrid.py.

As range separating function the Slater-function, \(\omega_\mathrm{RSF} = e^{(-\gamma r_{12})}\), is used. Besides the possibility to set \(\gamma\) to an arbitrary value, the following functionals were implemented:

Functional	\(\alpha\)	\(\beta\)	\(\gamma\) (\(a_0^{-1}\))	Reference
CAMY-BLYP	0.2	0.8	0.44	[AT08]
CAMY-B3LYP	0.19	0.46	0.34	[SZ12]
LCY-BLYP	0.0	1.0	0.75	[SZ12]
LCY-PBE	0.0	1.0	0.75	[SZ12]

As the implementation of RSFs in gpaw is based on the finite difference exact exchange code (hybrid.py), the implementation inherits its positive and negative properties, in summary:

self-consistent calculations using RSFs
calculations can only be done for the \(\Gamma\) point
only non-periodic boundary conditions can be used
only RMMDIIS can be used as eigensolver

Important: As one of the major benefits of the RSF is to retain the \(\frac{1}{r}\) asymptote of the exchange potential, one has to use large boxes if neutral or anionic systems are considered. Large boxes start at 6Å vacuum around each atom. For anionic systems “large” should be extended.

Further information about the implementation and RSFs can be found in [WW18] and in detail in [Wu16].

Simple usage¶

In general calculations using RSF can simply be done choosing the appropriate functional as in the following snippet:

"""First example for using RSF."""
from ase import Atoms
from gpaw import GPAW
from gpaw.eigensolvers import RMMDIIS
from gpaw.occupations import FermiDirac

h = 0.30
co = Atoms('CO', positions=[(0, 0, 0), (0, 0, 1.15)])
co.center(5)
# c = {'energy': 0.005, 'eigenstates': 1e-4}  # Usable values
c = {'energy': 0.1, 'eigenstates': 3, 'density': 3}  # Values for test
calc = GPAW(mode='fd', txt='CO.txt', xc='LCY-PBE', convergence=c,
            eigensolver=RMMDIIS(), h=h,
            occupations=FermiDirac(width=0.0), spinpol=False)
co.calc = calc
co.get_potential_energy()

Three main points can be seen already in this small snippet. Even if choosing the RSF is quite simple by choosing xc=LCY-PBE, one has to choose RMMDIIS as eigensolver, eigensolver=RMMDIIS(), and has to decrease the convergence criteria a little.

Improving results¶

However, there are a few drawbacks, at first in an SCF calculation the contributions from the core electrons are also needed, which have to be calculated during the generation of the PAW datasets. Second: for the calculation of the exchange on the Cartesian grid, the (screened) Poisson equation has to be solved numerically. For a charged system, as f.e. the exchange of a state with itself, one has to neutralize the charge by subtracting a Gaussian representing the “over-charge”, solve the (screened) Poisson-equation for the neutral system and add the solution for the Gaussian to the solution for the neutral system. However, if the charge to remove is “off-center”, the center of the neutralizing charge should match the center of the “over-charge” preventing an artificial dipole. The latter is done by using a Poisson solver which uses the charge center for removal: poissonsolver=PoissonSolver(use_charge_center=True). The next listing shows these two steps:

"""Calculations using RSF with dedicated datasets and Poisson-solver."""
from ase import Atoms
from gpaw import GPAW, setup_paths
from gpaw.poisson import PoissonSolver
from gpaw.eigensolvers import RMMDIIS
from gpaw.occupations import FermiDirac
from gpaw.test import gen

if setup_paths[0] != '.':
    setup_paths.insert(0, '.')

for atom in ['C', 'O']:
    gen(atom, xcname='PBE', scalarrel=True, exx=True,
        yukawa_gamma=0.75)

h = 0.30
co = Atoms('CO', positions=[(0, 0, 0), (0, 0, 1.15)])
co.center(5)

# c = {'energy': 0.005, 'eigenstates': 1e-4}  # Usable values
c = {'energy': 0.1, 'eigenstates': 3, 'density': 3}  # Values for test

calc = GPAW(mode='fd', txt='CO.txt', xc='LCY-PBE', convergence=c,
            eigensolver=RMMDIIS(), h=h,
            poissonsolver=PoissonSolver(use_charge_center=True),
            occupations=FermiDirac(width=0.0), spinpol=False)
co.calc = calc
co.get_potential_energy()

The generation of PAW-datasets can also be done by gpaw-setup -f PBE -x --gamma=0.75 C O

Tuning \(\gamma\)¶

As stated in the introduction, the optimal value for \(\gamma\) is under discussion. One way to find the optimal value for \(\gamma\) for ionization potentials is to tune \(\gamma\) in a way, that the negative eigenvalue of the HOMO matches the calculated IP. To use different values of \(\gamma\), one has to pass the desired value of \(\gamma\) to the variable omega.

"""Calculation utilizing RSF with optimized gamma."""
from ase import Atoms
from gpaw import GPAW, setup_paths
from gpaw.poisson import PoissonSolver
from gpaw.eigensolvers import RMMDIIS
from gpaw.occupations import FermiDirac
from gpaw.test import gen

# IP for CO using LCY-PBE with gamma=0.81 after
# dx.doi.org/10.1021/acs.jctc.8b00238
IP = 14.31

if setup_paths[0] != '.':
    setup_paths.insert(0, '.')

for atom in ['C', 'O']:
    gen(atom, xcname='PBE', scalarrel=True, exx=True,
        yukawa_gamma=0.81)

h = 0.30
co = Atoms('CO', positions=[(0, 0, 0), (0, 0, 1.15)])
co.center(vacuum=5)

# c = {'energy': 0.005, 'eigenstates': 1e-4}  # Usable values
c = {'energy': 0.1, 'eigenstates': 3, 'density': 3}  # Values for test

calc = GPAW(mode='fd', txt='CO.txt', xc='LCY-PBE:omega=0.81', convergence=c,
            eigensolver=RMMDIIS(), h=h,
            poissonsolver=PoissonSolver(use_charge_center=True),
            occupations=FermiDirac(width=0.0), spinpol=False)
co.calc = calc
co.get_potential_energy()
(eps_homo, eps_lumo) = calc.get_homo_lumo()
assert abs(eps_homo - -IP) < 0.35

linear response TDDFT¶

One of the major benefits of RSF is their ability to describe long-range charge transfer by linear response time-dependent DFT (lrTDDFT). If one uses RSF with lrTDDFT one has at least to activate the use of the Fock operator (FO) on the unoccupied states. Also the charge centered compensation of the over charge should be activated, see [Wu16] for details. The use of the FO on the unoccupied states is activated by the keyword unocc=True as in the following code:

"""Check TDDFT ionizations with Yukawa potential."""
from ase.structure import molecule
from ase.units import Hartree
from gpaw import GPAW
from gpaw.mpi import world
from gpaw.utilities.adjust_cell import adjust_cell
from gpaw.occupations import FermiDirac
from gpaw.eigensolvers import RMMDIIS
from gpaw.lrtddft import LrTDDFT

h2o = molecule('H2O')
h2o.set_initial_magnetic_moments([2, -1, -1])
adjust_cell(h2o, 3.0, h=0.3)
h2o_plus = molecule('H2O')
h2o_plus.set_initial_magnetic_moments([2, -0.5, -0.5])
adjust_cell(h2o_plus, 3.0, h=0.3)


def get_paw():
    """Return calculator object."""
    c = {'energy': 0.001, 'eigenstates': 0.001, 'density': 0.001}
    return GPAW(mode='fd', convergence=c, eigensolver=RMMDIIS(),
                xc='LCY-PBE:omega=0.83:unocc=True',
                parallel={'domain': world.size}, h=0.3,
                occupations=FermiDirac(width=0.0, fixmagmom=True))


calc = get_paw()
calc.set(txt='H2O_LCY_PBE_083.log')
calc_plus = get_paw()
calc_plus.set(txt='H2O_plus_LCY_PBE_083.log', charge=1)

h2o.calc = calc
e_h2o = h2o.get_potential_energy()
h2o_plus.calc = calc_plus
e_h2o_plus = h2o_plus.get_potential_energy()
e_ion = e_h2o_plus - e_h2o

print(e_ion, 12.62)
assert abs(e_ion - 12.62) < 0.1
lr = LrTDDFT(calc_plus, txt='LCY_TDDFT_H2O.log', jend=4)
assert lr.xc.omega == 0.83
lr.write('LCY_TDDFT_H2O.ex.gz')
# reading is problematic with EXX on more than one core
if world.rank == 0:
    lr2 = LrTDDFT('LCY_TDDFT_H2O.ex.gz')
    lr2.diagonalize()
    assert lr2.xc.omega == 0.83

    for i, ip_i in enumerate([14.74, 18.51]):
        ion_i = lr2[i].get_energy() * Hartree + e_ion
        print(ion_i, ip_i)
        assert abs(ion_i - ip_i) < 0.6

[AT08]

Akinaga and S. Ten-no. Range-separation by the Yukawa potential in long-range corrected density functional theory with Gaussian-type basis functions. Chemical Physics Letters 462.4 (10. Sep. 2008), S. 348–351.

[SZ12] (1,2,3)

Seth and T. Ziegler. Range-Separated Exchange Functionals with Slater-Type Functions. J. Chem. Theory Comput. 8.3 (2012), S. 901–907.

[Wu16] (1,2)

Würdemann. Berechnung optischer Spektren und Grundzustandseigenschaften neutraler und geladener Moleküle mittels Dichtefunktionaltheorie, PhD-Thesis. doi: 10.6094/UNIFR/11315

[WW18]

Würdemann and M. Walter. Charge Transfer Excitations with Range Separated Functionals Using Improved Virtual Orbitals. J. Chem. Theory Comput. 14.7 (2018), S. 3667-3676