# Solution of N2 exercise: Handling the log files¶

## Exercise¶

Compare the values of the HOMO and HOMO-1 eigenvalues for the LDA and the HF run. Change the values of the hgrid and crmult to find the converged values.

Note that, both in the LDA and in the HF calculation, a norm-conserving PSP is used.

The results can be compared to all-electron calculations, done with different basis sets, from references (units are eV) (1) S. Hamel et al. J. Electron Spectrospcopy and Related Phenomena 123 (2002) 345-363 and (2) P. Politzer, F. Abu-Awwad, Theor. Chem. Acc. (1998), 99, 83-87:

eigenvalues

LDA(1)

HF(1)

HF(2)

(Exp.)

3σg

10.36

17.25

17.31

(15.60)

1πu

11.84

16.71

17.02

(16.98)

2σu

13.41

21.25

21.08

(18.78)

The results depends, of course, on the precision chosen for the calculation, and of the presence of the pseudopotential. As it is well-known, the pseudopotential appoximation is however much less severe than the approximation induced by typical XC functionals. We might see that, even in the HF case, the presence of a LDA-based pseudopotential (of rather good quality) does not alter so much the results.

Here you can find the values from BigDFT calculation using a very good precision (hgrid=0.3, crmult=7.0). Note that 1 ha=27.21138386 eV.

eigenvalues

LDA

HF

3σg

10.41

16.82

1πu

11.88

17.37

2σu

13.42

21.18

How much do these values differ from the calculation with default parameters? Do they converge to a given value? What is the correlation for the N2 molecule in (PSP) LDA?

[1]:

from BigDFT.Systems import System
from BigDFT.Fragments import Fragment

N2 = System()
N2["N:0"] = Fragment(xyzfile=ifile)

[2]:

from BigDFT.Calculators import SystemCalculator
from BigDFT.Logfiles import Logfile
from BigDFT.Inputfiles import Inputfile

HtoeV = 27.21138386 #Conversion Hartree to meV

inp = Inputfile()
inp['psppar.N']={'Pseudopotential XC': 1}
study = SystemCalculator(skip=True, verbose=False) #Create a calculator

#Run the code with the name scheme LDA
inp.set_xc('LDA')
LDA = study.run(input=inp, name="LDA",posinp=N2.get_posinp(), run_dir="work")

#Run the code with the name scheme HF
inp.set_xc('HF')
HF = study.run(input=inp, name="HF",posinp=N2.get_posinp(), run_dir="work")

#Run the code with the name scheme HF
inp.set_xc('PBE0')
PBE0 = study.run(input=inp, name="PBE0",posinp=N2.get_posinp(), run_dir="work")


The variables first, LDA, HF and PBE0 are instances of the class BigDFT.Logfiles.Logfile which contain all information as the total energy. We should also use directly this call loading the corresponding output file as

[3]:

PBE0 = Logfile("work/log-PBE0.yaml")


We compare the values for LDA and HF.

[4]:

from pandas import DataFrame, options
df = DataFrame(columns=["eigenvalues", "LDA", "HF"])
options.display.float_format = '{:,.2f}'.format

lda_evals = LDA.evals[0][0]
hf_evals = HF.evals[0][0]

df.loc[0] = ["3$\sigma_g$", lda_evals[-1]*HtoeV, hf_evals[-1]*HtoeV]
df.loc[1] = ["1$\pi_u$", lda_evals[-3]*HtoeV, hf_evals[-3]*HtoeV]
df.loc[2] = ["2$\sigma_u$", lda_evals[-4]*HtoeV, hf_evals[-4]*HtoeV]

display(df)

eigenvalues LDA HF
0 3$\sigma_g$ -10.39 -16.80
1 1$\pi_u$ -11.86 -17.36
2 2$\sigma_u$ -13.41 -21.17

## Modifications of the calculation parameters¶

Then we do a convergence curve varying hgrid which controls the grid step of the Daubechies basis set and crmult the extension. The default values are:

[5]:

print('hgrids',LDA.log['dft']['hgrids'])
print('rmult',LDA.log['dft']['rmult'])

hgrids [0.45, 0.45, 0.45]
rmult [5.0, 8.0]


hgrids is an array of 3 values for the x, y, and z direction. A simple scalar can be indicated for the input. rmult is composed into two multiplied factors, one for the coarse grid, and the second one for the fine grid. We build our script for LDA and run it (on one core, it takes 10 minutes roughly for all calculations).

[6]:

Hgrids = [0.45, 0.40, 0.35, 0.30, 0.25]
Crmult = [3.0, 5.0, 7.0, 9.0]
log_LDA = {}

inp.set_xc('LDA')

emin_LDA = 0.0
for crmult in Crmult:
log_LDA[crmult] = []
for hgrid in Hgrids:
inp.set_hgrid(hgrid)
inp['dft']['rmult'] = [ crmult, 8.0]
name = "LDA-%4.2f-%04.1f" % (hgrid,crmult)
log = study.run(input=inp, name=name, posinp=N2.get_posinp(), run_dir="work")
log_LDA[crmult].append( log )
emin_LDA =min(emin_LDA,log.energy)


We do the same loops to run the Hartree-Fock calculations.

[7]:

log_HF = {}
inp.set_xc("HF")

emin_HF = 0.0
for crmult in Crmult:
log_HF[crmult] = []
for hgrid in Hgrids:
inp.set_hgrid(hgrid)
inp['dft']['rmult'] = [ crmult, 8.0]
name = "HF-%4.2f-%04.1f" % (hgrid,crmult)
log = study.run(input=inp, name=name, posinp=N2.get_posinp(), run_dir="work")
log_HF[crmult].append(log)
emin_HF = min(emin_HF,log.energy)

[8]:

import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# Lists of markers and colors (for matplotlib)
colors = ['#74a9cf', '#2b8cbe', '#045a8d', '#009900', '#FF8000']
colors = ['#000000', '#ff0000', '#045a8d', '#009900', '#FF8000']
markers = ['o','s','d','d','d']

plt.figure(figsize=(15,7))
# Plot with matplotlib
for i,crmult in enumerate(Crmult):
im = i%len(colors)
ener = [ HtoeV*(l.energy-emin_LDA) for l in log_LDA[crmult] ]
plt.plot(Hgrids, ener, marker=markers[im],
ls='-', label=str(crmult),color=colors[im])

plt.yscale('log')
plt.xlabel('Grid step (Bohr)')
plt.ylabel('Total energy $\Delta E$ (eV)')
plt.title('Dissociation energy of the N2 dimer for different rmult')
plt.legend(loc=4)
plt.show()

[9]:

plt.figure(figsize=(15,7))
# Plot with matplotlib
for i,crmult in enumerate(Crmult):
im = i%len(colors)
ener = [ HtoeV*(l.energy-emin_HF) for l in log_HF[crmult] ]
plt.plot(Hgrids, ener, marker=markers[im],
ls='-', label=str(crmult),color=colors[im])

plt.yscale('log')
plt.xlabel('Grid step (Bohr)')
plt.ylabel('Total energy $\Delta E$ (eV)')
plt.title('Dissociation energy of the N2 dimer for different crmult')
plt.legend(loc=4)
plt.show()


In order to converge the result, you need to decrease the grid step and also increase the extension of the mesh. For a given crmult, the curve are almost flat. For a hgrid value of of 0.35, there is few difference between the values with crmult=5.0 and 7.0 but for a hgrid value of 0.20 it is important. Now we give the HOMO-1 and HOMO eigenvalues both for LDA and HF functionals

[10]:

ih = Hgrids.index(0.3)
lda_evals = log_LDA[7.0][ih].evals[0][0]
hf_evals = log_HF[7.0][ih].evals[0][0]

df = DataFrame(columns=["LDA", "HF"])
for idx, (l,h) in enumerate(zip(reversed(lda_evals),reversed(hf_evals))):
df.loc[idx] = [-HtoeV*l, -HtoeV*h]

display(df)

LDA HF
0 10.41 16.82
1 11.88 16.82
2 11.88 17.37
3 13.42 21.18
4 28.35 40.21