Author: olunet

Uni-, false, and true bifunctional oxygen catalysis

Publishing as chapters in books does now always attract the deserved attention. That might be the case with a chapter I co-authored: R. Cepitis, A. Kosimov, V. Ivaništšev, and N. Kongi, in Multi-functional ElectrocatalystsFundamentals and Applications, ed. V. S. Saji and V. K. Pillai, Royal Society of Chemistry, 2024, vol. 46, ch. 13, pp. 357-374. https://drive.google.com/file/u/0/d/1_oLCbMU6vxPsQe93HitgP-rm6bFSvED_/view

It is worth reading for at least two reasons. First it gives a nice overview of uni-, false, and true bifunctional oxygen catalysis in just one Figure:

Second, it provides a dictionary for theoreticians and experimentalists to allow them to talk about workflows in electrocatalysis:

Uniting “simulants” over pizza

This semester I am co-organising a seminar on computer simulations (3 ECTS, LOTI.05.076). One of the aim is to gather and unite researchers from different institutes. Our common topic is using computers in research, so we are “simulants”, i.e. simulating reality via calculations. Some of core organisers are pictured in the centre, from left to right: Taavi Repän, Tauno Tiirats, Veronika Zadin, and Juhan Matthias Kahk.

My first talk was about running simulations on HPC, e.g. using apptainers. Probably because of free pizza there were two–three dozen of participants from institutes of Chemistry, Physics, and Technology, which is a surprisingly high number for the university of Tartu. It is a great start and I am looking forward to contribute more into strengthening collaboration between the institutes.

Colors in ASE

Updated colors for atoms in ASE in 2024 look like this:

For POV rendering there are several options: ASE2, ASE3, Glass, Glass2, Intermediate, JMOL, Pale, Simple, VMD. I like intermediate because it does not have an reflection and glare.

List of banned tells for GPT

1) Just watched Andy Stapleton youtube-video about echo technique for working with chatGPT. After that I have made my list of banned tells for GPT. Here it is.

You must not use these words and phrases: Vibrant, Bustling, Vital, Out of the box, Underscores, Dive into, Reverberate, Delve, Hustle and bustle, Foster, Labyrinthine, Moist, Remnant, Nestled, Game changer, Symphony, Gossamer, Enigma, A testament to, Indelible, Meticulous, Meticulously, Navigating, Complexities, Realm, Tailored, Underpins, Everchanging, Ever-evolving, Not only, Embark, Designed to enhance, It is advisable, Daunting,  In the realm of, Unlock the secrets, Unveil the secrets, Diving, Unleash, Harness, Tapestry, Take a dive into, Leverage, Metropolis, Unless, Arguably, To put it simply, Promptly, In today's digital era. Avoid analogies and ambiguity in wording. Avoid using gerunds, mdash, and other ways of extending sentences; instead split a sentence and consider removing the unnecessary second part. Avoid words that non-native speakers may not understand. Avoid jargon, but do keep all my terms – do not substitute technical terms with analogies. Avoid comparative adjectives and remove adjectives that do not shape the meaning. 

2) Previously I also used this prompt for working with LaTeX codes, but now I simply use texGPT in overleaf for quick drafting.
Start every sentence on a new line. Add line breaks after every 60 characters to make the text easier to read, but do not add these breaks within commented text. Preserve all original comments exactly as written, and do not delete anything unless explicitly instructed.

3) When working within a project, I define instructions like this:

Prohibited words and phrases: You must not use these words and phrases: Vibrant, Bustling, Vital, Out of the box, Underscores, Dive into, Reverberate, Delve, Hustle and bustle, Foster, Labyrinthine, Moist, Remnant, Nestled, Game changer, Symphony, Gossamer, Enigma, A testament to, Indelible, Meticulous, Meticulously, Navigating, Complexities, Realm, Tailored, Underpins, Everchanging, Ever-evolving, Not only, Embark, Designed to enhance, It is advisable, Daunting,  In the realm of, Unlock the secrets, Unveil the secrets, Diving, Unleash, Harness, Tapestry, Take a dive into, Leverage, Metropolis, Unless, Arguably, To put it simply, Promptly, In today's digital era. Avoid analogies and ambiguity in wording. Avoid using gerunds, mdash, and other ways of extending sentences; instead split a sentence and consider removing the unnecessary second part. Avoid words that non-native speakers may not understand. Avoid jargon, but do keep all my terms – do not substitute technical terms with analogies. Avoid comparative adjectives and remove adjectives that do not shape the meaning. 
You must avoid using "potential" in the sense of showing the capacity to develop into something in the future, because "electric potential" is a physical quantity. you must avoid "framework" in the sense of a basic structure underlying a computations, because "organic framework" is a term in material science.  You must avoid using "capacity" and "capacitance" in sense of ability to do something, because similar terms are used in electrochemistry. You must avoid using "generation" in sense of production.

Faculty interview

Recently I have been preparing for an interview for a faculty position. As usual I watched a lot of videos, read some tutorials, chatted with GPT, and talked to people (to whom I am very grateful).

The most insightful video turned out to be by Wenhao Sun UM. See below. It is about having a solid research vision!

Besides I would highly recommend a youtube channel “Life in academia” of a very positive prof. Matthias Rillig.

P.S. Despite preparation, I have not anticipated even a single question.

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