Author: olunet

My first presentation generated with AI

I am working on a presentation and use AI for the first time to draft some texts.

There are already existing solutions. Like plug-ins for google slides and copilot that works with powerpoint. I am using chatGPT and work in google slides.

Here is how I add some slides through app scripts:

function addSlide() {
    const presentationName = "ScientificActivities";
    const presentations = DriveApp.getFilesByName(presentationName);
    if (!presentations.hasNext()) return;

    const presentation = SlidesApp.openById(presentations.next().getId());
    const layouts = presentation.getMasters()[0].getLayouts();
    const targetLayout = layouts.find(layout => layout.getLayoutName() === "OBJECT");
    if (!targetLayout) return;

    const titleText = "Bridging Modelling and Scalable Chemical Processes"; // Edit title here
    const bodyText = "Assoc. Prof. Vladislav Ivaništšev"; // Edit body content here

    const newSlide = presentation.appendSlide(targetLayout);
    newSlide.getPlaceholder(SlidesApp.PlaceholderType.TITLE).asShape().getText().setText(titleText);
    newSlide.getPlaceholder(SlidesApp.PlaceholderType.BODY).asShape().getText().setText(bodyText);
}

Title and body can be generated by … within a long conversation with my personal research assistants. I enjoy the process because these skills will save me a lot of time in the future, although right now it took me some hours to get this simple code working. I have also used AI to find and download a dozen on logos of universities that I have visited (shown below). And it saved me time to get facts from my CV right into the slides.

Working with cubes

Working with cubes can be tedious. I need to show a change in electronic density of a MOF. For that I made two cubes for neutral and charged MOF. Then took their difference using cube_tools, like this.

import numpy as np
from cube_tools import cube

# Load the cube files using the cube class
cube1 = cube('mof_opt_0.0.cube')
cube2 = cube('mof_opt_2.0.cube')

# Subtract the data from cube1 from cube2
cube_diff = cube('mof_opt_2.0.cube')
cube_diff.data = cube2.data - cube1.data

# Get z-axis data and find indices where z > 13.3 (jellium density)
z_indices_above_threshold = np.where(cube_diff.Z > 13.3)[0]

# Remove densities above z = 13.3 by setting them to zero
for idx in z_indices_above_threshold:
    cube_diff.data[:, :, idx] = 0

# Save the modified cube data to a new file
cube_diff.write_cube('cdd.cube')

Once I have got the charge density difference and opened it in VMD, I realised that one part of my MOF is right at the border of a periodic cell, so that part of density was split. So, I used a terminal command to shift the cube like this “cube_tools -t -24 -36 0 cdd.cube”. I had to shift manually positions of the atoms by taking into account the voxels size. Next challenge was hiding half of my MOF to clear the view. So I used this tcl syntax in VMD:

vmd > mol clipplane normal 0 0 0 {1 0 0}
vmd > mol clipplane center 0 0 0 {3 0 0}
vmd > mol clipplane status 0 0 0 1
vmd > mol clipplane normal 0 1 0 {1 0 0}
vmd > mol clipplane center 0 1 0 {3 0 0} 
vmd > mol clipplane status 0 1 0 1
vmd > mol clipplane normal 0 2 0 {1 0 0}
vmd > mol clipplane center 0 2 0 {3 0 0}
vmd > mol clipplane status 0 2 0 1

Here is the result – density is almost homogeneously spread over my MOF upon charging.

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