from gpaw.xc.fxc_correlation_energy import FXCCorrelation
import numpy as np
a0 = 5.43
Ni = bulk('Ni', 'fcc')
Ni.set_initial_magnetic_moments([0.7])
kpts = monkhorst_pack((3,3,3))
calc = GPAW(mode='pw',
kpts=kpts,
occupations=FermiDirac(0.001),
setups={'Ni': '10'},
communicator=serial_comm)
Ni.set_calculator(calc)
E = Ni.get_potential_energy()
calc.diagonalize_full_hamiltonian(nbands=50)
rpa = RPACorrelation(calc)
E_rpa = rpa.get_rpa_correlation_energy(ecut=50,
skip_gamma=True,
gauss_legendre=8)
fxc = FXCCorrelation(calc, xc='RPA')
E_fxc = fxc.get_fxc_correlation_energy(ecut=50,
skip_gamma=True,
gauss_legendre=8)
equal(E_rpa, -7.826, 0.01)
equal(E_fxc, -7.827, 0.01)
from gpaw.xc.rpa_correlation_energy import RPACorrelation
import numpy as np
ecut = 25
N2 = molecule('N2')
N2.center(vacuum=2.0)
calc = GPAW(mode='pw', dtype=complex, xc='PBE', communicator=serial_comm)
N2.set_calculator(calc)
E_n2_pbe = N2.get_potential_energy()
E_n2_hf = E_n2_pbe + calc.get_xc_difference(HybridXC('EXX',
etotflag=True))
calc.diagonalize_full_hamiltonian(nbands=100)
rpa = RPACorrelation(calc, vcut='3D')
E_n2_rpa = rpa.get_rpa_correlation_energy(ecut=ecut,
directions=[[0, 2/3.], [2, 1/3.]],
gauss_legendre=8)
# -------------------------------------------------------------------------
N = molecule('N')
N.set_cell(N2.cell)
calc = GPAW(mode='pw', dtype=complex, xc='PBE', communicator=serial_comm)
N.set_calculator(calc)
E_n_pbe = N.get_potential_energy()
E_n_hf = E_n_pbe + calc.get_xc_difference(HybridXC('EXX',
etotflag=True))
calc.diagonalize_full_hamiltonian(nbands=100)
from __future__ import print_function
# Refer to J. Harl, et.al, PRB 81, 115126 (2010)
# Table III for parameter used in this calculation
# Fig. 2 to compare the correlation energy.
import numpy as np
from gpaw import GPAW
from gpaw.xc.rpa_correlation_energy import RPACorrelation
from gpaw.mpi import serial_comm, size
from ase.parallel import paropen
tag = 'Nabulk'
f = paropen('%s_RPA.dat' %(tag), 'a')
for i in range(9):
ecut = 120 + 10 * i
calc = GPAW('gs_%s.gpw' %(tag), communicator=serial_comm, txt=None)
rpa = RPACorrelation(calc, txt='rpa_%s_%s.txt' %(tag,ecut))
E = rpa.get_rpa_correlation_energy(ecut=ecut,
skip_gamma=False,
directions=[[0,1.0]],
kcommsize=size,
dfcommsize=size)
print('%s '%(ecut), E, file=f)
f.close()
from ase import *
from ase.parallel import paropen
from gpaw import *
from gpaw.mpi import serial_comm
from gpaw.xc.rpa_correlation_energy import RPACorrelation
calc = GPAW('gs_12_lda.gpw', communicator=serial_comm, txt=None)
rpa = RPACorrelation(calc, txt='rpa_12_lda.txt')
#for ecut in [100,150,200,250,300,350,400]:
for ecut in [400, 450, 500, 550]:
E = rpa.get_rpa_correlation_energy(ecut=ecut,
directions=[[0, 1.0]],
kcommsize=32)
f = paropen('rpa_12_lda.dat', 'a')
print >> f, ecut, E
f.close()
from ase import Atoms
from gpaw import GPAW, PW
from gpaw.mpi import serial_comm
from gpaw.test import equal
from gpaw.xc.rpa_correlation_energy import RPACorrelation
ecut = 50
He = Atoms('He')
He.center(vacuum=1.0)
calc = GPAW(mode=PW(force_complex_dtype=True),
xc='PBE',
communicator=serial_comm)
He.set_calculator(calc)
He.get_potential_energy()
calc.diagonalize_full_hamiltonian()
rpa = RPACorrelation(calc)
C6_rpa, C6_0 = rpa.get_C6_coefficient(ecut=ecut, direction=2)
equal(C6_0, 1.772, 0.01)
equal(C6_rpa, 1.387, 0.01)
from ase import *
from ase.parallel import paropen
from gpaw import *
from gpaw.mpi import serial_comm
from gpaw.xc.rpa_correlation_energy import RPACorrelation
ecut = 200
ds = [1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 5.0, 6.0, 10.0]
for d in ds:
calc = GPAW("gs_%s.gpw" % d, communicator=serial_comm, txt=None)
rpa = RPACorrelation(calc, txt="rpa_%s_%s.txt" % (ecut, d))
E_rpa = rpa.get_rpa_correlation_energy(
ecut=ecut,
frequency_cut=800,
frequency_scale=2.5,
kcommsize=128,
skip_gamma=True,
# directions=[[0, 2/3.], [2, 1/3.]],
restart="restart_%s_%s.txt" % (ecut, d),
)
f = paropen("rpa_%s.dat" % ecut, "a")
print >> f, d, E_rpa
f.close()
from ase import *
from ase.parallel import paropen
from gpaw import *
from gpaw.mpi import serial_comm
from gpaw.xc.rpa_correlation_energy import RPACorrelation
calc1 = GPAW('N.gpw', communicator=serial_comm, txt=None)
calc2 = GPAW('N2.gpw', communicator=serial_comm, txt=None)
rpa1 = RPACorrelation(calc1, txt='rpa_N.txt')
rpa2 = RPACorrelation(calc2, txt='rpa_N2.txt')
f = paropen('rpa_N2.dat', 'w')
for ecut in [100, 150, 200, 250, 300, 350, 400]:
E1 = rpa1.get_rpa_correlation_energy(ecut=ecut,
directions=[[0, 1.0]])
E2 = rpa2.get_rpa_correlation_energy(ecut=ecut,
directions=[[0, 2/3.], [2, 1/3.]])
print >> f, ecut, E2 - 2*E1
f.close()
#!/usr/bin/env python
from ase import *
from ase.parallel import paropen
from gpaw import *
from gpaw.mpi import serial_comm
from gpaw.xc.rpa_correlation_energy import RPACorrelation
w = np.linspace(0.0, 200.0, 32)
calc1 = GPAW('N_4000.gpw', communicator=serial_comm, txt=None)
calc2 = GPAW('N2_4000.gpw', communicator=serial_comm, txt=None)
rpa1 = RPACorrelation(calc1, txt='rpa_N.txt')
rpa2 = RPACorrelation(calc2, txt='rpa_N2.txt')
for ecut in [150, 200, 250, 300, 350, 400]:
E1 = rpa1.get_rpa_correlation_energy(ecut=ecut, w=w)
E2 = rpa2.get_rpa_correlation_energy(ecut=ecut, w=w)
f = paropen('rpa_atomization.dat', 'a')
print >> f, ecut, bands, E2 - 2 * E1
f.close()
#!/usr/bin/env python
from ase import *
from ase.parallel import paropen
from gpaw import *
from gpaw.mpi import serial_comm
from gpaw.xc.rpa_correlation_energy import RPACorrelation
w = np.linspace(0.0, 200.0, 32)
calc1 = GPAW('N_4000.gpw', communicator=serial_comm, txt=None)
calc2 = GPAW('N2_4000.gpw', communicator=serial_comm, txt=None)
rpa1 = RPACorrelation(calc1, txt='rpa_N.txt')
rpa2 = RPACorrelation(calc2, txt='rpa_N2.txt')
for ecut in [150, 200, 250, 300, 350, 400]:
E1 = rpa1.get_rpa_correlation_energy(ecut=ecut, w=w)
E2 = rpa2.get_rpa_correlation_energy(ecut=ecut, w=w)
f = paropen('rpa_atomization.dat', 'a')
print >> f, ecut, bands, E2 - 2*E1
f.close()
from ase import *
from ase.parallel import paropen
from gpaw import *
from gpaw.mpi import serial_comm
from gpaw.xc.rpa_correlation_energy import RPACorrelation
calc = GPAW('N2.gpw', communicator=serial_comm, txt=None)
rpa = RPACorrelation(calc, txt='rpa_N2_frequencies.txt')
f = paropen('con_freq.dat', 'w')
for N in [4, 6, 8, 12, 16, 24, 32]:
E = rpa.get_rpa_correlation_energy(ecut=200,
gauss_legendre=N,
directions=[[0, 2/3.], [2, 1/3.]])
print >> f, N, E
f.close()
from ase import *
from ase.parallel import paropen
from gpaw import *
from gpaw.mpi import serial_comm
from gpaw.xc.rpa_correlation_energy import RPACorrelation
import numpy as np
ws = np.array([0.5*i for i in range(2000)])
ecut = 50
calc = GPAW('N2.gpw', communicator=serial_comm, txt=None)
rpa = RPACorrelation(calc, txt='frequency_equidistant.txt')
Es = rpa.get_E_q(ecut=ecut,
w=ws,
integrated=False,
q=[0,0,0],
direction=0)
f = paropen('frequency_equidistant.dat', 'w')
for w, E in zip(ws, Es):
print >> f, w, E.real
f.close()
from ase import *
from ase.structure import bulk
import numpy as np
from gpaw import *
from gpaw.mpi import serial_comm
from gpaw.test import equal
from gpaw.xc.rpa_correlation_energy import RPACorrelation
calc = GPAW(h=0.18, xc='LDA', kpts=(4,4,4), #usesymm=None,
nbands=15, eigensolver='cg',
convergence={'bands': -5},
communicator=serial_comm)
V = 30.
a0 = (4.*V)**(1/3.)
Kr = bulk('Kr', 'fcc', a=a0)
Kr.set_calculator(calc)
Kr.get_potential_energy()
ecut = 30.
w = np.linspace(0.0, 50., 8)
rpa = RPACorrelation(calc)
E_rpa = rpa.get_rpa_correlation_energy(ecut=ecut, w=w)
equal(E_rpa, -3.2, 0.1)
