Author: olunet

Some tests with GFN2-xTB

GFN2-xTB [10.1021/acs.jctc.8b01176] is a strange model. I have been testing GFN1 and GFN2 on OOH adsorption on Pt(111). GFN1 from TBLITE with ASE works well. It converges and optimizes to meaningful structures. GFN2 however behaves odd in terms of convergence and optimization. For instance, O–H bond becomes broken. I have tested GFN2 also with xtb, for which the input is quite complicated in comparison to ASE inputs. Anyway, it worked only when I specified the periodic conditions in both xtb.inp and Pt-OOH.coord files. Then I executed xtb like this:

xtb Pt-OOH.coord --gfn2 --tblite --opt --periodic --input xtb.inp
Optimization of Pt(111)–OOH with GFN2-xTB (xtb) resulting in O–H bond dissociation.

P.S. You can see that Pt(111) surface corrugates in case of my 2×2 model. For wider models, the surface remains flat.

Set of useful soft for a PhD student

Today we installed some software on a laptop of our first year student:

  • Avogadro for quick drawing of chemical structures.
  • PovRay for rending high-quality figures.
  • Gimp for editing raster graphics.
  • Inkscape for editing vector graphics.
  • PDFGear for working with pdfs.
  • Zotero for bibliography management.

In case GPAW is ahead of ASE

When next time (like in 2024), GPAW refers to a beta-version of ASE to that

conda install -c conda-forge gpaw
conda remove --force ase
pip install --upgrade git+https://gitlab.com/ase/ase.git@master

Bader and Hirshfeld charges with python

Bader analysis is a fast and simple way of getting atomic charges. It is especially useful for periodic calculations. The analysis can be done on the fly using pybader tool from pypi.org/project/pybader. I recommend using it within conda environments.

The installation is straightforward:

pip install pybader

The usage is less obvious. Here is a function for obtaining xmol-type xyz that can obtained with GPAW and visualized with ASE:


def xyzb(atoms, filename, nCPU):
  from pybader.io import gpaw
  from pybader.interface import Bader
  import os
  bader = Bader(*gpaw.read_obj(atoms.calc)) # read ASE object 'atoms'
  bader(threads=nCPU)                       # specify the number of CPUs
  f = open('{0}.xyz'.format(filename), 'w') # set xmol format
  b = bader.atoms_charge                    # get number of electrons per atom
  n = atoms.get_atomic_numbers()            # get atomic numbers
  a = atoms.get_chemical_symbols()          # get chemical symbols
  p = atoms.get_positions()                 # get positions of the atoms
  f.write('{0}\n'.format(len(a)))
  f.write('Properties=species:S:1:pos:R:3:charge:R:1\n') # ensure compatibility with ASE
  for i in range(len(a)):                   # print symbol, positions, and charge
    s = '{0}'.format(a[i])
    x = '{0:.6f}'.format(round(p[i][0],6))
    y = '{0:.6f}'.format(round(p[i][1],6))
    z = '{0:.6f}'.format(round(p[i][2],6))
    c = '{0:.3f}'.format(round(float(n[i]) - float(b[i]),3))
    f.write('{0:<4}{1:>16}{2:>16}{3:>16}{4:>10}\n'.format(s,x,y,z,c))
  f.close()
  del bader
  os.remove('bader.p')

Similarly one can obtain xmol-type xyz file with Hirshfeld charges:


def xyzh(atoms, filename):
  from gpaw.analyse.hirshfeld import HirshfeldPartitioning
  f = open('{0}.xyz'.format(filename), 'w') # set xmol format
  a = atoms.get_chemical_symbols()          # get chemical symbols
  p = atoms.get_positions()                 # get positions of the atoms
  h = HirshfeldPartitioning(atoms.calc).get_charges()
  f.write('{0}\n'.format(len(a)))
  f.write('Properties=species:S:1:pos:R:3:charge:R:1\n') # ensure compatibility with ASE
  for i in range(len(a)):                   # print symbol, positions, and charge
    s = '{0}'.format(a[i])
    x = '{0:.6f}'.format(round(p[i][0],6))
    y = '{0:.6f}'.format(round(p[i][1],6))
    z = '{0:.6f}'.format(round(p[i][2],6))
    c = '{0:.3f}'.format(round(h[i],3))
    f.write('{0:<4}{1:>16}{2:>16}{3:>16}{4:>10}\n'.format(s,x,y,z,c))
  f.close()

In the LCAO mode of GPAW one can also get the Mulliken charges. Test before using:


def xyzm(atoms, filename):
  from gpaw.lcao.tools import get_mulliken
  f = open('{0}.xyz'.format(filename), 'w') # set xmol format
  a = atoms.get_chemical_symbols()          # get chemical symbols
  p = atoms.get_positions()                 # get positions of the atoms
  m = get_mulliken(atoms.calc, range(len(a)))
  f.write('{0}\n'.format(len(a)))
  f.write('Properties=species:S:1:pos:R:3:charge:R:1\n') # ensure compatibility with ASE
  for i in range(len(a)):                   # print symbol, positions, and charge
    s = '{0}'.format(a[i])
    x = '{0:.6f}'.format(round(p[i][0],6))
    y = '{0:.6f}'.format(round(p[i][1],6))
    z = '{0:.6f}'.format(round(p[i][2],6))
    c = '{0:.3f}'.format(round(m[i],3))
    f.write('{0:<4}{1:>16}{2:>16}{3:>16}{4:>10}\n'.format(s,x,y,z,c))
  f.close()

ILMAT5 presentation

LINK to the PRESENTATION

Abstract

In this work [0], we have applied the DFT-based delta Kohn–Sham (ΔKS) method to ion pairs in a vacuum to obtain X-ray pho­toelectron spectra of corresponding ionic liquids (IL). On the example of forty ion pairs, we demonstrate how the core level binding energy (BE) values can be calcu­lated and used to plot theo­retical spectra at a low computational cost. Furthermore, we compare the ΔKS results, 1s Kohn–Sham orbital energies, and atomic charges against the experi­mental X-ray photoelec­tron data. Recently, in connection to the electro­chemical application in the super­capacitors, we have measured spectra for EMImBF4 and EMImB(CN)4 ionic liquids at the carbon–IL interface [1–3]. Other experimental spectra were obtained from the literature [4,5]. Both the ΔKS BE values and the 1s Kohn–Sham orbital energies show a correlation, yet with a different order of the BEs assigned to spe­cific atoms. We find that neither DDEC6 nor Bader charges cor­relate with the experi­mental data. Thus, the DFT calculations of 1s Kohn–Sham orbital energies provide the fastest way of pre­dicting the XPS spectra. However, more detailed experimental studies are required to resolve the right order of the BE values and its rela­tion to the atomistic structure of the ILs. The ΔKS calculations provide the most precise estimations of the BEs. Herewith, they also demand more resources and cause computa­tional difficulties discussed in the presenta­tion. Besides the prediction power, a robust computational method can help in intepre­tating experimental data when the appropriate reference values are either not available nor directly applicable. Thus, the ΔKS method can find its application in various fields of physics and chemistry where the XPS is used for re­solving electronic and geometric structures of pure ILs, their mixtures, and at interfaces.

In this work, we have applied the DFT-based delta Kohn–Sham (ΔKS) method to ion pairs in a vacuum to obtain X-ray pho­toelectron spectra of corresponding ionic liquids (IL). On the example of forty ion pairs, we demonstrate how the core level binding energy (BE) values can be calcu­lated and used to plot theo­retical spectra at a low computational cost. Furthermore, we compare the ΔKS results, 1s Kohn–Sham orbital energies, and atomic charges against the experi­mental X-ray photoelec­tron data. Recently, in connection to the electro­chemical application in the super­capacitors, we have measured spectra for EMImBF4 and EMImB(CN)4 ionic liquids at the carbon–IL interface [1–3]. Other experimental spectra were obtained from the literature [4,5]. Both the ΔKS BE values and the 1s Kohn–Sham orbital energies show a correlation, yet with a different order of the BEs assigned to spe­cific atoms. We find that neither DDEC6 nor Bader charges cor­relate with the experi­mental data. Thus, the DFT calculations of 1s Kohn–Sham orbital energies provide the fastest way of pre­dicting the XPS spectra. However, more detailed experimental studies are required to resolve the right order of the BE values and its rela­tion to the atomistic structure of the ILs. The ΔKS calculations provide the most precise estimations of the BEs. Herewith, they also demand more resources and cause computa­tional difficulties discussed in the presenta­tion. Besides the prediction power, a robust computational method can help in intepre­tating experimental data when the appropriate reference values are either not available nor directly applicable. Thus, the ΔKS method can find its application in various fields of physics and chemistry where the XPS is used for re­solving electronic and geometric structures of pure ILs, their mixtures, and at interfaces.

[0] M. Lembinen, E. Nõmmiste, H. Ers, B. Docampo‐Álvarez, J. Kruusma, E. Lust, V.B. Ivaništšev, Calculation of core‐level electron spectra of ionic liquids, Int. J. Quantum Chem. 120 (2020). https://doi.org/10.1002/qua.26247.

[1] J. Kruusma, A. Tõnisoo, R. Pärna, E. Nõmmiste, I. Tallo, T. Romann, E. Lust, Electrochimica Acta 206 (2016) 419–426.

[2] J. Kruusma, A. Tõnisoo, R. Pärna, E. Nõmmiste, I. Kuusik, M. Vahtrus, I. Tallo, T. Romann, E. Lust, J. Electrochem. Soc. 164 (2017) A3393–A3402.

[3] A. Tõnisoo, J. Kruusma, R. Pärna, A. Kikas, M. Hirsimäki, E. Nõmmiste, E. Lust, J. Electrochem. Soc. 160 (2013) A1084–A1093.

[4] A. Foelske-Schmitz, D. Weingarth, R. Kötz, Surf. Sci. 605 (2011) 1979–1985.

[5] I.J. Villar-Garcia, E.F. Smith, A.W. Taylor, F. Qiu, K.R.J. Lovelock, R.G. Jones, P. Licence, Phys. Chem. Chem. Phys. 13 (2011) 2797–2808.

Oriental fonts in Ubuntu

!/bin/bash

for i in fonts-kacst fonts-kacst-one fonts-khmeros-core fonts-lklug-sinhala fonts-guru fonts-nanum fonts-noto-cjk fonts-takao-pgothic fonts-tibetan-machine fonts-guru-extra fonts-lao fonts-sil-padauk fonts-sil-abyssinica fonts-tlwg-* fonts-lohit-* fonts-beng fonts-beng-extra fonts-gargi fonts-gubbi fonts-gujr fonts-gujr-extra fonts-kalapi fonts-lohit-gujr fonts-samyak-* fonts-noto-unhinted fonts-noto-hinted fonts-navilu fonts-nakula fonts-orya-extra fonts-pagul fonts-sahadeva fonts-sarai fonts-smc fonts-telu-extra fonts-wqy-microhei; do
sudo apt purge -y $i
echo
done

echo “==== Fixing font cache”
sudo fc-cache -f -v && sudo dpkg-reconfigure fontconfig

echo “==== Packages remained (each containing multiple fonts)”
dpkg -l fonts*|grep ^ii|awk ‘{print $2}’

echo
read -p “Press any key to close.”

Installing the metalwalls

Here is a quick and dirty instruction for installation of the metalwalls code.

Download the source code using https protocol:

git clone https://gitlab.maisondelasimulation.fr/amarinla/mw2.git

You will be prompted for your username and password.

Preinstall gfortran and openmpi. Then copy compile.something.mk from the computer directory to the mw2 folder as compile.mk. Add a line “FPPFLAGS := -DMW_USE_MPI”. Comment the path to the pFUnit package.

Execute make.

Go to tests directory and execute tests:

python regression_tests.py -s nist

python regression_tests.py -s nacl

python regression_tests.py -s lammps

MD simulation of BMPyrDCA between graphene walls

Simple demonstration of a molecular dynamics simulation of 408 BMPyrDCA ionic pairs between two graphene walls.

Inputs (packmol.inp, STEEP.mdp, RUN.mdp, topol.top) and force field parameters: github.com/vilab-tartu/LOKT.02.048/tree/master/MD_Gr-BMPyrDCA_pbc. The force fields are taken from github: github.com/vladislavivanistsev/RTIL-FF. References are given within the files.

Continue reading “MD simulation of BMPyrDCA between graphene walls” →

MD simulation of bulk BMPyrDCA ionic liquid

Simple demonstration of a molecular dynamics simulation of 25 BMPyrDCA ionic pairs in a box.

Inputs (packmol.inp, STEEP.mdp, RUN.mdp, topol.top) and force field parameters: github.com/vilab-tartu/LOKT.02.048/tree/master/MD_BMPyrDCA_box. The force fields are taken from github: github.com/vladislavivanistsev/RTIL-FF. References are given within the files.

Continue reading “MD simulation of bulk BMPyrDCA ionic liquid” →