Making an overview presentation of the scaling relations

The following video-presentation – for the CHEAC Summer school 2025 – retells our review on the scaling relations electrocatalysis https://chemrxiv.org/engage/chemrxiv/article-details/67ed469081d2151a02b33a98

The final video

From the beginning I decided to try AI to prepare the presentation. Eventually the only to record the video turned out to be by the traditional way. Together with co-authors Ritums and Nadezda, we used PowerPoint with its slide-by-slide recording feature. As we were in 3 different locations, we exchanged the presentation several time while recording. I used chatgpt 4o and 5 to write lecturer’s notes for every slide. In particular, I gave the chat our article’s pdf-file and then discussed every slide-text using canvas-feature to polish it iteratively. Nadezda also used chatgpt to refined her slides before reading them aloud. Overall, I have spend over two weeks planning the presentation. Then a week polishing the slides. Then several days to record and re-record slides. And finally I have got this the final video-presentation.

Adding voice to a ready presentation

app.pictory.ai does a relatively good job on reading the lecturer’s notes in a ready presentation. Thought, it reads “Jan” and “OOH” in a funny way. And it adds a lot of 10–20 second pauses. Also the slide numbering is off as well as all animation. The picture is also cut from below. But overall, it takes around 2 hours to generate this voiced video and process it.

Use NotebookLM to make a podcast

In the prompt I have specified to avoid banned tells, see https://doublelayer.eu/vilab/2024/12/17/list-of-banned-tells-for-gpt/ Well, I have forbidden to use “pivotal”, but AI still uses “pivotal”.

I am not responsible for the result 🙂 I have heard it and it sounds OK-ish.

Using Gemini in Google Slides

Does not work for me. Gemini wants to draw images. I just want to enter my own figures.

All I need is to convert Figures to Slides

https://www.magicslides.app promises to do exactly that but I failed with a notice that below 5 Mb files are allowed.

SlideAI extension also does not do what I want.

Ufff … manual upload is still the fastest and most robust. Well, it is not so simple, as most of my figures are in pdf, so I wrote this script to convert everything to png. When it took me 2 mins to drag-and-drop all png figure to my presentation. Hurray!

#!/bin/bash

# Create output folder
mkdir -p png

# List of input files
files=(
"Figure 1 mechanisms.png"
"Figure 18 Timeline.png"
"FIgure 14 distances.pdf"
"Figure 11 relative.pdf"
"Figure 6 3dvolcano_withscaling.pdf"
"Figure 2 publications.pdf"
"Figure 5 3dvolcano.pdf"
"Figure 17 perspectives.pdf"
"Figure 16 O_bypassing.pdf"
"Figure 15 O_pushing.pdf"
"Figure 12 O_breaking.pdf"
"Figure 13 O_switching.png"
"Figure 10 O_tuning.pdf"
"Figure 7 projection_potential.pdf"
"Figure 9 projection_ads.pdf"
"Figure 8 timeline.pdf"
"Figure 3 ass_diss.png"
"Figure 4 scalings.png"
)

# Loop through files
for f in "${files[@]}"; do
  base=$(basename "$f")
  name="${base%.*}"
  ext="${base##*.}"
  
  if [[ "$ext" == "pdf" ]]; then
    convert -density 300 "$f" -quality 100 "png/${name}.png"
  elif [[ "$ext" == "png" ]]; then
    cp "$f" "png/${name}.png"
  else
    echo "Unsupported file type: $f"
  fi
done

Use NotebookLM to create FAQ

Pretty cool – NotebookLM make a FAQ.

What are scaling relations in electrocatalysis, and why are they important?

Scaling relations are correlations between the adsorption energies of reaction intermediates on a catalyst’s surface. They are crucial in multi-step electrocatalytic reactions, such as the oxygen reduction reaction (ORR), carbon dioxide reduction (CO2R), and nitrogen reduction (N2RR). The concept emerged in 2005 with the discovery of linear relations between adsorption energies of intermediates like OH, OOH, and O on metal surfaces. Understanding these relations is vital because they define fundamental chemical limitations in electrocatalytic reactions, impacting the design of more efficient catalysts for energy conversion technologies like electrolysers, fuel cells, and metal-air batteries.

How do scaling relations limit the efficiency of oxygen electrocatalysis?

In oxygen electrocatalysis, particularly the oxygen reduction reaction (ORR), the adsorption energies of key intermediates (OOH, OH, O) are correlated by scaling relations. These correlations constrain the achievable catalytic activity, often visualised on “volcano plots.” The OOH-OH and O-OH scaling relations, for instance, mean that if a catalyst binds one intermediate optimally, it might bind another too strongly or too weakly, preventing it from reaching the ideal catalytic activity (the “volcano top”). This limitation is significant, as experimental results have shown catalytic overpotentials converging to a limit set by these relations for over two decades, hindering progress in sustainable energy solutions.

What are the main reaction mechanisms in oxygen electrocatalysis, and how does catalyst geometry influence them?

Oxygen electrocatalysis primarily proceeds via two mechanisms: associative and dissociative. The associative mechanism, which dominates most known catalysts, involves intermediates like OOH, OH, and O adsorbing at a single active site. Geometrically, this requires only one atom in the active site. The dissociative mechanism, conversely, requires at least two neighbouring atoms to accommodate dissociation products (O and OH). On metal surfaces, a spatial mismatch often prevents the dissociative mechanism, as O preferentially adsorbs on hollow sites and OH on top sites. However, dual-atom site catalysts (DACs) can facilitate dissociative pathways by providing two adjacent sites, allowing for the adsorption of dissociation products. The inter-atomic distance within these active sites is a critical geometric parameter that influences the energy barrier for dissociation, balancing thermodynamics and kinetics.

What is the “volcano plot” in electrocatalysis, and how do scaling relations affect it?

The “volcano plot” is a theoretical framework used to understand electrocatalysis, typically representing overpotential or activity as an “altitude” against adsorption energy descriptors. For ORR, it correlates adsorption energies with deviations from the thermodynamic equilibrium potential. Scaling relations define the “paths” or “fixed climbing routes” on this volcano plot that are accessible to catalysts. For example, the OOH-OH scaling relation appears as a plane on the three-dimensional volcano, and catalysts following this relation are confined to a specific line on the volcano’s surface. This means that while an “ideal catalyst” (the volcano’s apex) might exist theoretically, scaling relations prevent most catalysts from reaching it, limiting the search for optimal catalysts to a two-dimensional projection.

What are the five general strategies for “manipulating” scaling relations in electrocatalysis?

The review outlines five general strategies for manipulating scaling relations to enhance electrocatalytic performance:

  1. Tuning: Adjusting the adsorption energy of a key intermediate (e.g., ∆GOH) to optimise catalyst performance within the constraints of an existing scaling relation, adhering to the Sabatier principle.
  2. Breaking: Decreasing the intercept (β) of a scaling relation by selectively stabilising one intermediate over another (e.g., OOH relative to OH), often by introducing spectator groups that induce stabilising interactions.
  3. Switching: Changing the slope (α) of a scaling relation by enabling an alternative reaction mechanism (e.g., switching from an associative to a dissociative mechanism in ORR) to avoid problematic intermediates. This usually requires dual active sites.
  4. Pushing: A combined strategy that changes the slope and adjusts the intercept, simultaneously switching to an alternative mechanism and using stabilising interactions (similar to breaking).
  5. Bypassing: Completely decoupling adsorption energies by switching between two distinct states of the catalyst (e.g., geometric or electronic) during the reaction cycle, with each state having optimal adsorption energies for specific intermediates. This strategy aims to eliminate all scaling relation constraints.

How does the “breaking” strategy specifically aim to overcome the OOH-OH scaling relation?

The “breaking” strategy focuses on reducing the intercept of the OOH-OH scaling relation (from approximately 3.2 eV to an ideal value of 2.46 eV) by selectively stabilising the OOH intermediate relative to OH. This typically involves introducing spectator groups or a second adsorption site near the active site. These spectators can form hydrogen bonds or other stabilising interactions with OOH, effectively shifting its adsorption energy without proportionally affecting OH. While challenging to achieve experimentally, this strategy has been demonstrated in oxygen evolution reactions (OER) and more recently in ORR using dual-atom catalysts (DACs) with specific active sites like PN3FeN3, where the phosphorus acts as a spectator to stabilise OOH through hydrogen bonding.

What role do Single-Atom Site Catalysts (SACs) and Dual-Atom Site Catalysts (DACs) play in manipulating scaling relations?

Single-Atom Site Catalysts (SACs) and Dual-Atom Site Catalysts (DACs) are crucial in manipulating scaling relations due to their distinct geometric and electronic properties. SACs typically allow for “on-top” adsorption, primarily favouring the associative mechanism in ORR. DACs, with their two neighbouring active sites, offer the possibility of accommodating two dissociation products simultaneously, thereby enabling the dissociative mechanism. This ability to switch mechanisms is key to the “switching” strategy, where DACs can replace the OOH intermediate with two distinct O and OH intermediates adsorbed at separate sites. Furthermore, the precise control over inter-atomic distances and curvature in DACs allows for fine-tuning of electronic structures and promoting specific interactions (like hydrogen bonding), contributing to “breaking” and “pushing” strategies.

What is the ultimate goal of manipulating scaling relations, and how does the “bypassing” strategy contribute to this vision?

The ultimate goal of manipulating scaling relations is to achieve ideal catalyst performance, ideally with zero overpotential, by overcoming the fundamental limitations imposed by these correlations. The “bypassing” strategy represents the most ambitious approach towards this goal. It seeks to completely decouple the adsorption energies of reaction intermediates by allowing the catalyst to switch between two or more distinct states (e.g., geometric, electronic, or photonic) during the reaction cycle. Each state would be optimally configured to bind specific intermediates at the ideal energy values required for efficient catalysis. While seemingly challenging in practice, this concept, inspired by natural enzymes like cytochrome c oxidase, offers a theoretical pathway to eliminate all scaling constraints and achieve the theoretical apex of the volcano plot, pushing the boundaries of what is currently achievable in electrocatalysis.

Playing with GPAW in colab

To play with GPAW in colab “sandbox” install it like that:

!apt install libxc-dev
!pip install gpaw
!echo "y" | gpaw install-data /usr/local/pkgs/gpaw-data-0.9.20000/
setup_paths = ['/usr/local/pkgs/gpaw-data-0.9.20000/gpaw-setups-24.11.0/']

Playing with new xTB

https://chemrxiv.org/engage/chemrxiv/article-details/685434533ba0887c335fc974

# login to HPC
wget https://github.com/grimme-lab/g-xtb/archive/refs/tags/v1.0.0.tar.gz
tar -xvf v1.0.0.tar.gz
nano ~/.bashrc
add "export PATH=~/g-xtb-1.0.0/binary:$PATH"
exit .bashrc
source ~/.bashrc
cp -r ~/g-xtb-1.0.0/parameters/ ~/
# create a new conda environment
conda create gxtb
conda activate gxtb
conda install -c conda-forge libgfortran=14.2.0
conda install -c conda-forge xtb
# run
xtb example.xyz --driver "gxtb -grad -c xtbdriver.xyz" --optA quick test of g-xTB

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.

Zotero + chatGPT via pdfGEAR

Some time ago (in 2023), I linked Zotero with chatGPT by creating an environment with paper-qa and pyzotero like this:
conda create -n Zotero
conda activate Zotero
conda install pip
pip install paper-qa
pip install pyzotero
pip install bs4

That worked but felt way too complicated … like I am not going to use it on a daily basis. It also reminded me the very first experience with the Meta AI in late 2022 (which everyone already forgot).

Here is a much simpler recipe:

  1. Install Zotero add-on from github.com/retorquere/zotero-open-pdf to enable opening with external pdf viewers.
  2. Install pdfGEAR as your default pdf viewer (external to Zotero).

See how it works on my YouTube channel: youtu.be/4JSy2RsBLDE?si=Hbj7oq7gaOiq6END

Fonts for grant proposals

The reference font for the body text of European proposals is Times New Roman (Windows platforms), Times/Times New Roman (Apple platforms) or Nimbus Roman No. 9 L (Linux distributions). The Roman family is from a pre-digital age and has well-recognizable features.

Is it the best font in terms of readability? On the one hand, there is a tendency to move from Times-type fonts to plainer fonts, like Calibri. On the other hand, many studies (with controversial results) account for aspects like Dyslexia, typeface anatomy, and Display vs. Print. The effect of font choice on readability and compression on big numbers seems small or insignificant. However, my point is that a proposal must be clear to a few reviewers, who might have difficulties understanding the proposal due to age, Dyslexia, and colour vision deficiency. These few people will have some feelings about how the text is formatted. For that reason and also because of my artistic education in caligraphy, I have been looking for and playing with font combinations for a long time. Here is what I have tried and liked.

1. STIX two and Source Sans form a pair of Serif and Sans fonts. STIX two resulted from a collaborative effort from the most prominent academic publishing companies. Its predecessor (STIX one) has exactly the same metrics as Times New Roman. STIX two is somewhat bigger, which is not prohibited by the EU funding agencies. The main benefit of using STIX fonts is that these are mathematical fonts and, thus, can be natively used in MS Equation Editor (instead of Cambria) and LaTeX (as XITS or STIX2).

2. An excellent substitution for Times New Roman is Zilla Slab – a unique font by the Mozilla foundation – which has the same metrics as Times New Roman, is a Sans font, yet looks like a monospace one, does have features of a Dyslexia-friendly typeface, and looks great in print and on screen. It is freely available from Google fonts. It can be used with Times New Roman (or similar) as a pair of Serif and Sans fonts.

3. Libertinus Serif + Gill Sans is my favourite Serif and Sans pair. You can see Linux Libertine in the Wikipedia logo. Gill Sans Nova is commonly fond in the University of Tartu (Estonia) press. Although Libertinus Serif has an original Sans counterpart, its combination with Gill Sans looks most natural. I love Libertinus because of its amazingly looking ligatures, and it is also compatible with MS Equation Editor and LaTeX.

PS One can play with fonts in the EU projects to make their proposal more appealing. Like Estonian grants, I prefer calls, where applicants fill out online forms without changing the text appearance. Of course, the text looks ugly due to nasty line breaks, horrible chemical formulas and mathematical equations, and poor typography. Still, the competition is more fair because everyone is in the same conditions. 

Reference style for an MSCA proposal

Dani Bodor created a nice reference style that is suitable for writing an MSCA proposal:

marie-sklodowska-curie-actions.csl

See forums.zotero.org/discussion/73275/foot-notes-for-a-marie-curie-application.

This style is similar to what I used in my LaTeX template:

www.overleaf.com/latex/templates/msca-template/dskmdjpzbhkg

Here is the code for that style:

<?xml version="1.0" encoding="utf-8"?>
<style xmlns="http://purl.org/net/xbiblio/csl" class="note" version="1.0" demote-non-dropping-particle="never" page-range-format="chicago" default-locale="en-GB">
  <!-- This style was edited with the Visual CSL Editor (http://editor.citationstyles.org/visualEditor/) -->
  <info>
    <title>Marie Skłodowska-Curie actions</title>
    <id>http://www.zotero.org/styles/marie-sklodowska-curie-actions</id>
    <link href="http://www.zotero.org/styles/marie-sklodowska-curie-actions" rel="self"/>
    <link href="https://forums.zotero.org/discussion/comment/262041/#Comment_262041" rel="documentation"/>
    <author>
      <name>Dani Bodor</name>
      <email>d.bodor@ucl.ac.uk</email>
    </author>
    <category citation-format="note"/>
    <category field="generic-base"/>
    <summary>For use in application to H2020 Marie Skłodowska-Curie Actions fellowship</summary>
    <updated>2017-07-31T17:37:20+00:00</updated>
    <rights license="http://creativecommons.org/licenses/by-sa/3.0/">This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License</rights>
  </info>
  <locale xml:lang="en">
    <terms>
      <term name="editor" form="verb-short">ed.</term>
      <term name="translator" form="verb-short">trans.</term>
      <term name="editortranslator" form="verb-short">
        <single>ed. and trans.</single>
        <multiple>ed. and trans.</multiple>
      </term>
      <term name="editortranslator" form="verb">
        <single>Edited and translated by</single>
        <multiple>Edited and translated by</multiple>
      </term>
      <term name="translator" form="short">trans.</term>
    </terms>
  </locale>
  <macro name="secondary-contributors">
    <choose>
      <if type="chapter paper-conference" match="none">
        <names variable="editor translator" delimiter=". ">
          <label form="verb" text-case="capitalize-first" suffix=" "/>
          <name and="text" delimiter=", "/>
        </names>
      </if>
    </choose>
  </macro>
  <macro name="container-contributors">
    <choose>
      <if type="chapter paper-conference" match="any">
        <group delimiter=", ">
          <choose>
            <if variable="author">
              <choose>
                <if variable="container-author" match="any">
                  <names variable="container-author">
                    <label form="verb-short" text-case="lowercase" suffix=" "/>
                    <name and="text" delimiter=", "/>
                  </names>
                </if>
              </choose>
              <choose>
                <if variable="container-author author" match="all">
                  <group delimiter=". ">
                    <text variable="page"/>
                    <names variable="editor translator" delimiter=", ">
                      <label form="verb" suffix=" "/>
                      <name and="text" delimiter=", "/>
                    </names>
                  </group>
                </if>
                <else>
                  <names variable="editor translator" delimiter=", ">
                    <label form="verb" text-case="lowercase" suffix=" "/>
                    <name and="text" delimiter=", "/>
                  </names>
                </else>
              </choose>
            </if>
          </choose>
        </group>
      </if>
    </choose>
  </macro>
  <macro name="recipient-note">
    <names variable="recipient" delimiter=", ">
      <label form="verb" text-case="lowercase" suffix=" "/>
      <name and="text" delimiter=", "/>
    </names>
  </macro>
  <macro name="editor">
    <names variable="editor">
      <name name-as-sort-order="first" and="text" sort-separator=", " delimiter=", " delimiter-precedes-last="always"/>
      <label form="short" prefix=", "/>
    </names>
  </macro>
  <macro name="translator">
    <names variable="translator">
      <name name-as-sort-order="first" and="text" sort-separator=", " delimiter=", " delimiter-precedes-last="always"/>
      <label form="verb-short" prefix=", "/>
    </names>
  </macro>
  <macro name="recipient">
    <group delimiter=" ">
      <choose>
        <if type="personal_communication">
          <choose>
            <if variable="genre">
              <text variable="genre" text-case="capitalize-first"/>
            </if>
            <else>
              <text term="letter" text-case="capitalize-first"/>
            </else>
          </choose>
        </if>
      </choose>
      <text macro="recipient-note"/>
    </group>
  </macro>
  <macro name="contributors">
    <group delimiter=". ">
      <names variable="author">
        <name name-as-sort-order="first" and="text" sort-separator=", " delimiter=", " delimiter-precedes-last="always"/>
        <substitute>
          <text macro="editor"/>
          <text macro="translator"/>
        </substitute>
      </names>
      <text macro="recipient"/>
    </group>
  </macro>
  <macro name="recipient-short">
    <names variable="recipient">
      <label form="verb" text-case="lowercase" suffix=" "/>
      <name form="short" and="text" delimiter=", "/>
    </names>
  </macro>
  <macro name="contributors-short">
    <group delimiter=" ">
      <names variable="author">
        <name form="short" and="symbol"/>
        <substitute>
          <names variable="editor"/>
          <names variable="translator"/>
        </substitute>
      </names>
      <text macro="recipient-short"/>
    </group>
  </macro>
  <macro name="interviewer">
    <names variable="interviewer" delimiter=", ">
      <label form="verb" text-case="capitalize-first" suffix=" "/>
      <name and="text" delimiter=", "/>
    </names>
  </macro>
  <macro name="title">
    <choose>
      <if variable="title" match="none">
        <choose>
          <if type="personal_communication" match="none">
            <text variable="genre" text-case="capitalize-first"/>
          </if>
        </choose>
      </if>
      <else-if type="bill book graphic legislation motion_picture song" match="any">
        <text variable="title" text-case="title" font-style="italic"/>
        <group prefix=" (" suffix=")" delimiter=" ">
          <text term="version"/>
          <text variable="version"/>
        </group>
      </else-if>
      <else-if variable="reviewed-author">
        <group delimiter=", ">
          <text variable="title" font-style="italic" prefix="Review of "/>
          <names variable="reviewed-author">
            <label form="verb-short" text-case="lowercase" suffix=" "/>
            <name and="text" delimiter=", "/>
          </names>
        </group>
      </else-if>
      <else-if type="legal_case interview patent" match="any">
        <text variable="title"/>
      </else-if>
      <else>
        <text variable="title" text-case="title" quotes="true"/>
      </else>
    </choose>
  </macro>
  <macro name="description">
    <group delimiter=", ">
      <group delimiter=". ">
        <text macro="interviewer"/>
        <text variable="medium" text-case="capitalize-first"/>
      </group>
      <choose>
        <if variable="title" match="none"/>
        <else-if type="thesis speech" match="any"/>
        <else-if type="patent">
          <group delimiter=" ">
            <text variable="authority"/>
            <text variable="number"/>
          </group>
        </else-if>
        <else>
          <text variable="genre" text-case="capitalize-first"/>
        </else>
      </choose>
    </group>
  </macro>
  <macro name="container-title">
    <group delimiter=" ">
      <choose>
        <if type="chapter paper-conference" match="any">
          <text term="in" text-case="capitalize-first"/>
        </if>
      </choose>
      <choose>
        <if type="legal_case" match="none">
          <text variable="container-title" text-case="title" font-style="italic"/>
        </if>
      </choose>
    </group>
  </macro>
  <macro name="collection-title">
    <choose>
      <if match="none" type="article-journal">
        <choose>
          <if match="none" is-numeric="collection-number">
            <group delimiter=", ">
              <text variable="collection-title" text-case="title"/>
              <text variable="collection-number"/>
            </group>
          </if>
          <else>
            <group delimiter=" ">
              <text variable="collection-title" text-case="title"/>
              <text variable="collection-number"/>
            </group>
          </else>
        </choose>
      </if>
    </choose>
  </macro>
  <macro name="collection-title-journal">
    <choose>
      <if type="article-journal">
        <group delimiter=" ">
          <text variable="collection-title"/>
          <text variable="collection-number"/>
        </group>
      </if>
    </choose>
  </macro>
  <macro name="edition-note">
    <choose>
      <if type="bill book chapter graphic legal_case legislation motion_picture paper-conference report song" match="any">
        <choose>
          <if is-numeric="edition">
            <group delimiter=" ">
              <number variable="edition" form="ordinal"/>
              <text term="edition" form="short"/>
            </group>
          </if>
          <else>
            <text variable="edition"/>
          </else>
        </choose>
      </if>
    </choose>
  </macro>
  <macro name="edition">
    <choose>
      <if type="bill book chapter graphic legal_case legislation motion_picture paper-conference report song" match="any">
        <choose>
          <if is-numeric="edition">
            <group delimiter=" ">
              <number variable="edition" form="ordinal"/>
              <text term="edition" form="short"/>
            </group>
          </if>
          <else>
            <text variable="edition" text-case="capitalize-first" suffix="."/>
          </else>
        </choose>
      </if>
    </choose>
  </macro>
  <macro name="locators-note">
    <choose>
      <if type="article-journal">
        <group delimiter=", ">
          <text macro="collection-title-journal"/>
          <number variable="volume"/>
          <group delimiter=" ">
            <text term="issue" form="short"/>
            <number variable="issue"/>
          </group>
        </group>
      </if>
      <else-if type="legal_case">
        <group delimiter=", ">
          <group delimiter=" ">
            <number variable="volume"/>
            <text variable="container-title"/>
            <text variable="page"/>
          </group>
          <text variable="locator"/>
        </group>
      </else-if>
      <else-if type="bill book chapter graphic legal_case legislation motion_picture paper-conference report song" match="any">
        <group delimiter=", ">
          <text macro="edition-note"/>
          <group delimiter=" ">
            <text term="volume" form="short"/>
            <number variable="volume" form="numeric"/>
          </group>
          <choose>
            <if variable="locator" match="none">
              <group delimiter=" ">
                <number variable="number-of-volumes" form="numeric"/>
                <text term="volume" form="short" plural="true"/>
              </group>
            </if>
          </choose>
        </group>
      </else-if>
    </choose>
  </macro>
  <macro name="locators-join-with-space">
    <choose>
      <if type="article-journal" variable="volume" match="all">
        <choose>
          <if match="none" variable="collection-title">
            <text macro="locators"/>
          </if>
        </choose>
      </if>
    </choose>
  </macro>
  <macro name="locators-join-with-comma">
    <choose>
      <if type="legal_case chapter paper-conference" match="any">
        <text macro="locators"/>
      </if>
      <else-if type="article-journal">
        <choose>
          <if variable="volume" match="none">
            <text macro="locators"/>
          </if>
          <else-if match="any" variable="collection-title">
            <text macro="locators"/>
          </else-if>
        </choose>
      </else-if>
    </choose>
  </macro>
  <macro name="locators-join-with-period">
    <choose>
      <if type="legal_case article-journal chapter paper-conference" match="none">
        <text macro="locators"/>
      </if>
    </choose>
  </macro>
  <macro name="locators">
    <choose>
      <if type="article-journal">
        <group delimiter=", ">
          <text macro="collection-title-journal"/>
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PAW Setups & PseudoPotentials

GPAW has a large set of PAW setups (updated in 2016) for elements from H to Rn, excluding lanthanides, actinides, and radioactive elements. One can generate new setups with a PAW generating build-in tool and their own risk. One can use optimized norm-conserving Vanderbilt SG15 pseudopotentials (updated in 2017) or norm-conserving Hartwigsen-Goedecker-Hutter HGH pseudopotentials (see also GPAW intro) or even JTH pseudopotentials from ABINIT. There are even more setups, including f-elements, listed on the QE webpage. The great thing about these setups is that they use a similar format – either xml or upfApparently, GPAW can read both formats, although there is no relevant documentation. So, there are many ways to run calculations with elements that are missing in the GPAW default setups set. QuantumATK webpage provides an overview of pseudopotentials and even suggests mixing them. I hope that in the future, these and new PAWs will be gathered together like basis sets at the basis sets exchange portal.

P.S. Interesting https://esl.cecam.org/data/ and https://molmod.ugent.be/deltacodesdft

DFT geometry optimizers

These are undeservedly less attention to optimizers than density functionals (concerning Jacob’s ladder). It is not even covered in the recent review: Best-Practice DFT Protocols for Basic Molecular Computational Chemistry. At the same time, in my current projects, the most resource-demanding was geometry optimization – the time spent on optimizing structures was much longer than a single-point calculation. Papers that introduce new (AI-based) optimizers promise significant speed-up. However, there are always some problems:

  1. The tested systems are different from my electrochemical interfaces.
  2. The code is not available or difficult to install.
  3. The code is outdated and contains bugs.
  4. Optimizers perform worse than the common ones, like QuasiNewton in ASE.

ASE wiki lists all internal and some external optimizers and provides their comparison. I have checked the most promising on a high-entropy alloy slab.

Observation 1. QuasiNewton outperforms all other optimizers. Point. I have run a standard GPAW/DFT/PBE/PW optimization with various optimizers:

Observation 2. Pre-optimizing the slab with a cheaper method does not reduce the number of optimization steps. I have preoptimized the geometry with TBLITE/DFTB/GFN1-xTB to continue with GPAW/DFT/PBE/PW. Preoptimization takes just some minutes and the obtained geometry looks similar to the DFT one but that does not reduce the number of DFT optimization steps.

OptimizerN steps*Time$N steps*#Total time#
BFGS1602:44:271703:01:26
LBFGS1502:30:351602:55:04
BondMin1202:46:271302:45:07
GPMin1205:26:233108:14:22
MLMin38verylong2812:31:29
FIRE3805:06:564405:56:54
QuasiNewton801:36:23902:00:10

Note * – the printed number of steps might different from the actuall number of calculations because each calculator has a different way of reporting that number.

Note $ – the time between the end of the first and last steps.

Note # – started from the TBLITE/DFTB/GFN1-xTB preoptimized geometry.

N.B! I have done my test only once in two runs: starting with slab.xyz and preoptized geometry. Runs were on similar nodes and all optimizations were done on the same node.

Conclusion. Do not believe in claims in articles advertizing new optimizers – Run your tests before using them.

A practical finding. The usual problem with calculations that require many optimization steps is that they need to fit into HPC time limits. On the restart, ASE usually rewrites the trajectory. Some optimizers (GPMin and AI-based) could benefit from reading the full trajectory. So, I started writing two trajectories and a restart file like this.

# Restarting
if os.path.exists(f'{name}_last.gpw') == True and os.stat(f'{name}_last.gpw').st_size > 0:
    atoms,calc = restart(f'{name}_last.gpw', txt=None)
    parprint(f'Restart from the gpw geometry.')
elif os.path.exists(f'{name}_full.traj') == True and os.stat(f'{name}_full.traj').st_size > 0:
    atoms = read(f'{name}_full.traj',-1)
    parprint(f'Restart with the traj geometry.')
else:
    atoms = read(f'{name}_init.xyz')
    parprint(f'Start with the initial xyz geometry.')

# Optimizing
opt = QuasiNewton(atoms, trajectory=f'{name}.traj', logfile=f'{name}.log')
traj= Trajectory(f'{name}_full.traj', 'a', atoms)
opt.attach(traj.write, interval=1)
def writegpw():
    calc.write(f'{name}_last.gpw')
opt.attach(writegpw, interval=1)
opt.run(fmax=0.05, steps=42)

Here are some details on the tests.

My gpaw_opt.py for DFT calculations on 24 cores:

# Load modules
from ase import Atom, Atoms
from ase.build import add_adsorbate, fcc100, fcc110, fcc111, fcc211, molecule
from ase.calculators.mixing import SumCalculator
from ase.constraints import FixAtoms, FixedPlane, FixInternals
from ase.data.vdw_alvarez import vdw_radii
from ase.db import connect
from ase.io import write, read
from ase.optimize import BFGS, GPMin, LBFGS, FIRE, QuasiNewton
from ase.parallel import parprint
from ase.units import Bohr
from bondmin import BondMin
from catlearn.optimize.mlmin import MLMin
from dftd4.ase import DFTD4
from gpaw import GPAW, PW, FermiDirac, PoissonSolver, Mixer, restart
from gpaw.dipole_correction import DipoleCorrection
from gpaw.external import ConstantElectricField
from gpaw.utilities import h2gpts
import numpy as np
import os

atoms = read('slab.xyz')
atoms.set_constraint([FixAtoms(indices=[atom.index for atom in atoms if atom.tag in [1,2]])])

# Set calculator
kwargs = dict(poissonsolver={'dipolelayer':'xy'},
              xc='RPBE',
              kpts=(4,4,1),
              gpts=h2gpts(0.18, atoms.get_cell(), idiv=4),
              mode=PW(400),
              basis='dzp',
              parallel={'augment_grids':True,'sl_auto':True,'use_elpa':True},
             )
calc = GPAW(**kwargs)

#atoms.calc = SumCalculator([DFTD4(method='RPBE'), calc])
#atoms.calc = calc

# Optimization paramters
maxf = 0.05

# Run optimization
###############################################################################

# 2.A. Optimize structure using MLMin (CatLearn).
initial_mlmin = atoms.copy()
initial_mlmin.set_calculator(calc)
mlmin_opt = MLMin(initial_mlmin, trajectory='results_mlmin.traj')
mlmin_opt.run(fmax=maxf, kernel='SQE', full_output=True)

# 2.B Optimize using GPMin.
initial_gpmin = atoms.copy()
initial_gpmin.set_calculator(calc)
gpmin_opt = GPMin(initial_gpmin, trajectory='results_gpmin.traj', logfile='results_gpmin.log', update_hyperparams=True)
gpmin_opt.run(fmax=maxf)

# 2.C Optimize using LBFGS.
initial_lbfgs = atoms.copy()
initial_lbfgs.set_calculator(calc)
lbfgs_opt = LBFGS(initial_lbfgs, trajectory='results_lbfgs.traj', logfile='results_lbfgs.log')
lbfgs_opt.run(fmax=maxf)

# 2.D Optimize using FIRE.
initial_fire = atoms.copy()
initial_fire.set_calculator(calc)
fire_opt = FIRE(initial_fire, trajectory='results_fire.traj', logfile='results_fire.log')
fire_opt.run(fmax=maxf)

# 2.E Optimize using QuasiNewton.
initial_qn = atoms.copy()
initial_qn.set_calculator(calc)
qn_opt = QuasiNewton(initial_qn, trajectory='results_qn.traj', logfile='results_qn.log')
qn_opt.run(fmax=maxf)

# 2.F Optimize using BFGS.
initial_bfgs = atoms.copy()
initial_bfgs.set_calculator(calc)
bfgs_opt = LBFGS(initial_bfgs, trajectory='results_bfgs.traj', logfile='results_bfgs.log')
bfgs_opt.run(fmax=maxf)

# 2.G. Optimize structure using BondMin.
initial_bondmin = atoms.copy()
initial_bondmin.set_calculator(calc)
bondmin_opt = BondMin(initial_bondmin, trajectory='results_bondmin.traj',logfile='results_bondmin.log')
bondmin_opt.run(fmax=maxf)

# Summary of the results
###############################################################################

fire_results = read('results_fire.traj', ':')
parprint('Number of function evaluations using FIRE:',
         len(fire_results))

lbfgs_results = read('results_lbfgs.traj', ':')
parprint('Number of function evaluations using LBFGS:',
         len(lbfgs_results))

gpmin_results = read('results_gpmin.traj', ':')
parprint('Number of function evaluations using GPMin:',
         gpmin_opt.function_calls)

bfgs_results = read('results_bfgs.traj', ':')
parprint('Number of function evaluations using BFGS:',
         len(bfgs_results))

qn_results = read('results_qn.traj', ':')
parprint('Number of function evaluations using QN:',
         len(qn_results))

catlearn_results = read('results_mlmin.traj', ':')
parprint('Number of function evaluations using MLMin:',
         len(catlearn_results))

bondmin_results = read('results_bondmin.traj', ':')
parprint('Number of function evaluations using BondMin:',
         len(bondmin_results))

Initial slab.xyz file:

45
Lattice="8.529357696932532 0.0 0.0 4.264678848466266 7.386640443507905 0.0 0.0 0.0 29.190908217261956" Properties=species:S:1:pos:R:3:tags:I:1 pbc="T T F"
Ir       0.00000000       1.62473838      10.00000000        5
Ru       2.81412943       1.62473838      10.00000000        5
Pt       5.62825885       1.62473838      10.00000000        5
Pd       1.40706471       4.06184595      10.00000000        5
Ag       4.22119414       4.06184595      10.00000000        5
Ag       7.03532356       4.06184595      10.00000000        5
Ag       2.81412943       6.49895353      10.00000000        5
Ru       5.62825885       6.49895353      10.00000000        5
Pt       8.44238828       6.49895353      10.00000000        5
Pt       0.00000000       0.00000000      12.29772705        4
Ag       2.81412943       0.00000000      12.29772705        4
Ru       5.62825885       0.00000000      12.29772705        4
Ru       1.40706471       2.43710757      12.29772705        4
Ir       4.22119414       2.43710757      12.29772705        4
Ag       7.03532356       2.43710757      12.29772705        4
Ag       2.81412943       4.87421514      12.29772705        4
Ir       5.62825885       4.87421514      12.29772705        4
Pd       8.44238828       4.87421514      12.29772705        4
Pd       1.40706471       0.81236919      14.59545411        3
Ir       4.22119414       0.81236919      14.59545411        3
Pt       7.03532356       0.81236919      14.59545411        3
Ag       2.81412943       3.24947676      14.59545411        3
Ir       5.62825885       3.24947676      14.59545411        3
Ir       8.44238828       3.24947676      14.59545411        3
Pd       4.22119414       5.68658433      14.59545411        3
Pt       7.03532356       5.68658433      14.59545411        3
Ag       9.84945299       5.68658433      14.59545411        3
Pd       0.00000000       1.62473838      16.89318116        2
Pd       2.81412943       1.62473838      16.89318116        2
Ag       5.62825885       1.62473838      16.89318116        2
Pt       1.40706471       4.06184595      16.89318116        2
Ag       4.22119414       4.06184595      16.89318116        2
Ag       7.03532356       4.06184595      16.89318116        2
Ru       2.81412943       6.49895353      16.89318116        2
Ru       5.62825885       6.49895353      16.89318116        2
Ru       8.44238828       6.49895353      16.89318116        2
Ir       0.00000000       0.00000000      19.19090822        1
Ag       2.81412943       0.00000000      19.19090822        1
Pt       5.62825885       0.00000000      19.19090822        1
Pd       1.40706471       2.43710757      19.19090822        1
Ag       4.22119414       2.43710757      19.19090822        1
Pd       7.03532356       2.43710757      19.19090822        1
Ag       2.81412943       4.87421514      19.19090822        1
Ru       5.62825885       4.87421514      19.19090822        1
Ir       8.44238828       4.87421514      19.19090822        1

My tblite_opt.py for DFTB calcualation with just one core. It takes some minutes but eventually crashes 🙁

# Load modules
from ase import Atom, Atoms
from ase.build import add_adsorbate, fcc100, fcc110, fcc111, fcc211, molecule
from ase.calculators.mixing import SumCalculator
from ase.constraints import FixAtoms, FixedPlane, FixInternals
from ase.data.vdw_alvarez import vdw_radii
from ase.db import connect
from ase.io import write, read
from ase.optimize import BFGS, GPMin, LBFGS, FIRE, QuasiNewton
from ase.parallel import parprint
from ase.units import Bohr
from tblite.ase import TBLite
import numpy as np
import os

# https://tblite.readthedocs.io/en/latest/users/ase.html

atoms = read('slab.xyz')
atoms.set_constraint([FixAtoms(indices=[atom.index for atom in atoms if atom.tag in [1,2]])])

# Set calculator
calc = TBLite(method="GFN1-xTB",accuracy=1000,electronic_temperature=300,max_iterations=300)
atoms.set_calculator(calc)
qn_opt = QuasiNewton(atoms, trajectory='results_qn.traj', logfile='results_qn.log', maxstep=0.1)
qn_opt.run(fmax=0.1)

To compare structures I have used MDanalysis, which unfortunately does not work with ASE traj, so I prepared xyz-files with “ase convert -n -1 file.traj file.xyz”

import MDAnalysis as mda
from MDAnalysis.analysis.rms import rmsd
import sys

def coord(file_name):
    file  = mda.Universe(f"{file_name}.xyz")
    atoms = file.select_atoms("index 1:9")
    return  atoms.positions.copy()

print(rmsd(coord(sys.argv[1]),coord(sys.argv[2])))

An instruction on installation of GPAW. TBLITE can be installed as “conda install -c conda-forge tblite”.