# Tag: science communication

## TEDx Talk: The Virtual Lab

Happy to announce my TEDxUHasselt talk is officially part of the TEDx universe:  https://www.ted.com/talks/danny_vanpoucke_the_virtual_lab .

I enjoyed talking about the VirtualLab. Showed examples from atoms to galaxies, from computer-chips to drug-design and from to opinion-dynamics to epidemiology. I looked at the past and and glanced towards the future, where machine learning and artificial intelligence are the new kids on the block.

## TEDxUHasselt: Virtual Lab

Saturday March 20th at 19h00, I have the pleasure of speaking at the TEDxUHasselt 2021 event.

We will visit the virtual lab and I’ll dive into the important questions about computational researchers: Who are we?  Why do we like supercomputers…and rubber ducks? And what does the future hold?

Along the way, I’ll touch on the use of computational research for any subject imaginable: from atoms to galaxies, to the spread of diseases as well as opinions.

Update 22/03/2021:
The entire live-stream is still online available at here. (My presentation starts at ~7’30 😉 ).

## Gnuplot animated gifs: Visualizing Machine-Learning models

One of the most important aspects in machine-learning—in addition to the modeling itself—is undoubtedly visualization. This can be of either the data set itself or the resulting model. When dealing with small or sparse data sets and a limited number of features, visualization can be extremely helpful to get a feel for your model and data. In this tutorial, we show how you can use gnuplot to generate interesting animations of your data, such as the example above.

#### What do you need?

• Install Gnuplot  version 5.2.8 (or higher) for your OS (under windows you can also install it under your Cygwin installation)
• A data set as a simple multi-column text-file data.dat .
• A similar text-file, model.dat, with your model calculated on a grid .

#### 1. Starting simple: a static image

The main difference between an animation and a static image is the fact the former is just a series of such static images shown one after the other.

##### 1.a. Basic image

Gnuplot allows both interactive and scripted command-line usage. The commands used in interactive mode can simply be placed in a text-file (e.g., myplot.gpl) and run using  the command:

> gnuplot myplot.gpl

Comments can be added in such a file by preceding them with a single “#“. In the examples below, I’m using “###” as a personal choice. It shows clearly the location of the comments, and also gives me an easy way to distinguish with script lines I commented out for testing purposes, in which case I use a single #. In the following I also indicate gnuplot commands in red, while options are indicated in turquise. Let u start by plotting the data set in a simple png:

### Set the output to a png file
set termopt enhanced
set terminal pngcairo size 300,300 font "Helvetica-Bold,6"
### The file to write to
set output 'modelplot_v1.png'
### The Title label
set title 'ML model tutor' font "Helvetica-Bold,10"

splot "data.dat" u 1:2:3

Model v1

With this we set the output to be a png image of 300×300 pixels. (Note: pngcairo also provides png-functionality using the cairo-library. For more complex plotting, it gives much nicer images.) The default font for text is set to “Helvetical-Bold” with a font-size of 6pt. The enhanced option further allows us to use LaTeX type strings, for example indicating subscripts as A_n to print An. The resulting image is stored as ‘modelplot_v1.png‘.

The last two commands are used to create the actual plot. With set title a title can be added to the graph. The default font is replaced in this case by a slightly larger version of 10pt. The splot command  allows you to plot 3D surfaces using the same basic information as the gnuplot plot command for 2D plots. In this case, I used the first 3 columns of the data.dat file to plot 3D data, with the x:y:z giving the respective column numbers. The result is shown on the right.

NOTE: An important point to consider is the fact that the font size is absolute. So if you decide later-on to change your image size to say 500×500 pixels, your text labels may look rather small, and you will have to tweak the font-size to compensate of this behavior. Therefore, it is important to make sure you start with the right image size straight away. The 300×300 pixels used in this tutorial are too small for any scientific quality image, it was chosen to be a suitable image size to incorporate in this blog.

##### 1.b. Pimp the axis

With the basics for the graph set up, we can start setting up the graph to our liking.

###settings for the boxplot
set xlabel "M_{n polyX} (g/mol)" offset 0,-1,0 font "Helvetica-Bold, 8" rotate parallel
set xrange[1000:10000] noreverse writeback
set xtics 2000,2000,10000 out scale 1.0 nomirror offset 0,-0.5,0

set ylabel "Graft (%)" offset 2,0,0 font "Helvetica-Bold, 8" rotate parallel
set yrange[0:30] noreverse writeback
set ytics 0,10,30 out scale 1.0 nomirror offset 0,-0.5,0

set zlabel "Particle size (nm)" offset 1,1,0 font "Helvetica-Bold, 8" rotate parallel
set zrange[0:300] noreverse writeback
set ztics 0,50,300 out scale 1.0 nomirror offset 0,-0.5,0

set xyplane at 0
set border lw 3

Model v2

The label of each of the three axes can be modified individually using set {x/y/z}label followed by the same options available to any other string (such as the graph title earlier). Here you can see how the enhanced mode allows the use of a subscript using standard LaTeX formatting. The offset makes sure the axis-label does not overlap with the tick-labels. Gnuplot also allows you to define the range to be plotted using set {x/y/z}range[min:max], while set {x/y/z}tics gives you access to the specifics of the individual tics. The latter can be very useful to manually add specific tics, or, as in the current case, manually set the splitting between the different tics. The out option places the tic-marks at the outside of the graph, and their size is set by the scale option.

The command set xyplane can be used to set the intercept of the xy-plane and the z-axis, and set border gives access to the axis-line properties. Here I have set the line-width (lw) to 3.

##### 1.c. Add the (machine-learning) model

Now that the basics properties of the 3D graph are alright*, let us add the model to the plot. This can easily be done by just adding additional input for the splot command.

splot \
"data.dat" using 1:2:3 with points pointtype 7 pointsize 1 linecolor rgb "brown4" notitle, \
"model.dat" u 1:2:3 w line lc rgb "sea-green" notitle

Model v3

The “\” can be used to split the command to multiple lines. In this case, each curve/surface/data set is set on a separate line. Since the command for a single plot can become very long, gnuplot also has a shorthand for most common keywords/options it uses.  For the data the extended keywords are shown, and the shorthand is used for the model. The length of the command becomes significantly shorter, but at the same time harder to read. (Note that both shorthand as longhand keywords can be mixed in a single command.)

The data set is now being shown as points, using the 7th pointtype (which are discs). The size of these symbols is set to 1 and the linecolor is a predefined color used by gnuplot.  Finally, the notitle option removes the legend entry of this curve. The model data is presented as a line-surface. The end result is shown on the right.

##### 1.d. A better surface-plot: Multi-plot

Model v4

As you can see in the previous version of the plot, the model data is plotted as a surface, but this is not a very nice surface. This is because gnuplot just connected the sequential points in the file as a single very long and very complex curve. If you would rotate this plot, it would become clear, several things are very wrong. Luckily there is a very simple solution. Gnuplot has the ability to transform a point-cloud into a surface. This is done by setting a 3D grid using set dgrid3d X,Y. This creates a 3D surface for which the nodes are interpolated between the points of your point-cloud. When you set this option, it is applied on all data curves you plot (i.e., including the set of data-points, which we would like to avoid.). Using the multiplot option of gnuplot the two curves can be drawn separately, using different settings. In the script the splot command is replaced by:

### switch to a multiplot
set multiplot
set dgrid3d 50, 50
splot \
"model.dat" u 1:2:3 w line lc rgb "sea-green" notitle

unset dgrid3d
splot \
"data.dat" using 1:2:3 with points pointtype 7 pointsize 1 linecolor rgb "brown4" notitle

unset multiplot


By setting the multiplot environment, we can unset dgrid3d before drawing the second data set. At the end of script we also unset multiplot to switch of the multiplot environment. At this point it become interesting to see the impact of the terminal pngcairo over png.

##### 1.e. Surface coloring

Drawing a surface is nice, but you can also give it some color. Either by using the z-value as a color scale, or by using another metric/feature to color the surface.

###settings for the color scale
set colorbox vertical
set cblabel "Colormap\n (RGB)" font "Helvetica-Bold, 8" offset -6.75,8 rotate by 0
set pm3d at s explicit

splot \
"model.dat" u 1:2:3 w pm3d notitle


Model v5

Surface coloring is switched on via the command set pm3d which is set at the surface, and is used in splot at the with option. In addition to surface coloring also a color scale is added, with the label formatted using the same options as for other labels. To get the label above the color scale it needs to be shifted using the offset and the rotate option.

The result is rather fancy, but for practical purposes, the surface may actually block the view of the data points. This can be avoided by projecting the color on the xy-plane and retaining a grid representation of the surface. This is done by setting pm3d at the bottom.

Model v6

In addition, we  also need to plot the model surface twice, once to generate the color map and once to generate the model surface as a grid-image.

set pm3d at b explicit

splot \
"model.dat" u 1:2:3 w pm3d notitle,\
"model.dat" u 1:2:3 w line lc rgb "sea-green" notitle

#### 2. Creating an animation

With gnuplot it is quite easy to generate stunning 3D animated gif images. Some nice examples can be found all over the web, such as this animated Bessel function, my own (very old) molecular d-and f-orbitals, or this collection. Once you finish creating a script to generate a single image, creating an animation requires only some minor modifications. First of all we need to select the correct terminal (i.e., gif instead of png)

set terminal gif transparent animate nooptimize delay 10 size 300,300 font "Helvetica-Bold,10"
set output 'modelplot_v7.gif'

This generates a transparent animated gif with a delay of 10 ms between frames, and stores it in a gif image. In addition, a change in time/image frame needs to be implemented. This can easily be done by a simple for loop, which is wrapped around the plotting section.

n=60
do for [i=1:n]{
set view 60, i*360/n

### do the multiplot plotting section
set multiplot
...all the other plotting stuff of before
unset multiplot
}
set output



model v7: animated

In the example, the 3D graph is rotated. This is done by changing the view via the set view command which takes two angles in degrees. As you can see from “i*360/n“, gnuplot also accepts simple mathematical equations.

Once the loop is finished we need to close our gif animation. This is done via (a side-effect of) the command set output. The set output {filename} command sets the output to a file with name filename, or if a filename is omitted to the standard output. As a side-effect it closes the current output file, c.q. our animated gif.

An alternative method for creating an animation would be creating a series of images (in the image-format of your choice, e.g. pngcairo and then create an apng) and combining them yourself or via additional scripting into an animated image format using additional software, such as is done here.

#### 3. Animated surfaces and coloring

The example above is rather trivial in regard to animations. The ability to perform math inside a gnuplot script provides you the ability to make things a lot more interesting. In the following, we are going to construct a small imaginary solar system, to present some of the things which are possible.

The basic script for the solar system above can be downloaded here.

set termopt enhanced
set terminal gif animate nooptimize delay 10 size 300,300 font "Helvetica,10"
set output 'Solarplot_v1.gif'
set title 'Magic Solar System' font "Helvetica-Bold,10"

maxl=10
set xrange[-maxl:maxl] noreverse writeback
set yrange[-maxl:maxl] noreverse writeback
set zrange[-maxl:maxl] noreverse writeback
set xyplane at 0
set border lw 1.5

###Use parametric coordinated for plotting spheres
set parametric # enable parametric mode with angles (u,v)
set urange [0:2*pi]
set vrange [-pi/2.0:pi/2.0]
set isosample 360,180
fx(u,v)=cos(u)*cos(v)
fy(u,v)=sin(u)*cos(v)
fz(v)=sin(v)

### Surface coloring
set colorbox vertical
set cblabel "Planet\n colors" font "Helvetia, 8" offset -4.75,5 rotate by 0
set pm3d depthorder base nohidden3d
unset hidden3d

### The animated drawing
n = 60
do for [i=1:n]{
# The star
x=0.0
y=0.0
z=0.0
r=3.0

# The first planet
x1=5.0*sin(i*2*pi/n)
y1=5.0*cos(i*2*pi/n)
z1=0.0
r1=0.50
color1(u,v)=0.5

# The second planet
x2=6.5*sin(i*2*pi/n)
y2=7.0*cos(i*2*pi/n)
z2=1.0*cos(i*2*pi/n)
r2=1.0
color2(u,v)=sin(v)*2*pi

splot \
"++" using (x+r*fx(u,v)):(y+r*fy(u,v)):(z+r*fz(v)):(6.5) w pm3d notitle,\
"++" using (x1+r1*fx(u,v)):(y1+r1*fy(u,v)):(z1+r1*fz(v)):(color1(u,v)) w pm3d notitle,\
"++" using (x2+r2*fx(u,v)):(y2+r2*fy(u,v)):(z2+r2*fz(v)):(color2(u,v)) w pm3d notitle
}
set output

Most of the commands and options have already been covered above. To draw our spherical planets, we introduce a set of parametric coordinates (u,v) via the command set parametric. Next we set their ranges, just as you would do for the x,y, and z coordinates via set {u|v}range. The command set isosample is used to define the grid over the parametric space.  With this setup, you can now define any parametric surface you want. In our case, we want to have a sphere. For this we define three transformation functions. With u and v representing the θ and φ angles of a sphere, the transformation to Cartesian coordinates is given by the functions fx(u,v),fy(u,v), and fz(v).

In the main loop of the gif we define the center position (x,y,z) and the radius (r) of our planets and star, with the latter nicely fixed at the origin, and our planets having an orbit around it. To have a nice periodic gif, you should make sure that any periodic behavior ends up where it started, hence the 2pi factor in the sines and cosines.  Everything is drawn with a single splot command where we use a pseudo 4-column input style:

(x-coord):(y-coord):(z-coord):(color-coord)

Note that the brackets ‘(‘ & ‘)‘ are important to include as gnuplot will throw errors otherwise. The selected color, can be either a real value in the color scale or a function. The resulting solar system is shown below on the left.

 Solar v1, no depth Solar v1, depth

Not that bad for a first attempt. There is however a small snag: the 3D effect is somehow off when the planets move behind the star. This is due to the depth-buffering. The newest versions of gnuplot (≥5.2.8) provide the option depthorder for the set pm3d command. Using the value base for the depthorder option results in the depthorder to be decided based on the z-projected position of the object. This is sufficient to fix our little solar system, as you can see on the right.

##### 3.a. Some cleanup work: removing the box and complex coloring

As we are creating an imaginary (magical) solar system, we should maybe get rid of the x-,y-, and z-axis. This is done via the commands unset border to get rid of the axis-bars and unset {x|y|z}tics to remove the tic-marks and-labels.

unset border
unset xtics
unset ytics
unset ztics
set palette defined (0 "red", 1 "yellow",1 "brown", 2 "brown4",3 "dark-green", 4 "blue", 5 "white")
set cbrange[0:10]
#unset cbtics

Solar v2

And although the color palette provided is nice, if we want different color schemes on each of the planets, we quickly run into a small problem: you can only have 1 color palette per splot. In case of a static image, you might be able to get around this problem by using a multiplot (as before) and have overlapping splots with each their own palette. But…in that case you will also be responsible for getting the 3D order of your objects correct yourself. And although this may be doable for a single frame, in case of an animated 3D solar system this will be a hassle nearly impossible to overcome**. For this you need to tackle the problem in a different way: create your own color palette consisting of sub-palettes. This can be done via the command set palette defined. The pairs give the color at the endpoints of gradient ranges, with the overall range (here 0-5) representing the entire color scale. The intermediate points are placed equidistant, so for a color range from 0:10 the red-to-yellow gradient is linked to color values in the range 0:2, while the blue-to-white gradient is linked to the color values in the range 8:10. Applying this to our solar system we can give nice individual color palettes to each of the objects. I changed the size and position of the three objects a bit, and as you can see, the outer planet moves outside of the x/y/z-ranges. Now that we know how to add different color palettes to each of our planets, we can also remove the color-bar on the right using the command unset colorbox, remove the tics via unset cbtics, and remove the label via unset cblabel.

##### 3.b. time-dependent colors

We have motion of objects and different color palettes, what about changing the colors during the animation? As we saw earlier, the color component can be defined as a function, which means we can make this time-dependent as well. Let’s imagine that our outer planet is traveling on a rather elliptic orbit, making it heat up when it approaches our star.

# The first planet
color1(u,v)=0.5*(cos(u)**5+sin(v)**3)+sin(i*6*pi/n) +8.0

# The second planet
x2=8+15*sin((i+20)*2*pi/n)
y2=6.0*cos((i+20)*2*pi/n)
z2=3.0*cos((i+20)*2*pi/n)
color2(u,v)=0.99*sin((i+20)*2*pi/n+pi)+1

By making the color dependent on the frame number, the (uniform) coloring of our second planet will now cycle through the red-yellow gradient. The first planet experiences a variation at 3x the speed but have a non-uniform surface coloring.

Solar v3

##### 3.c. time-dependent colors and shapes

Once you have time dependent coloring, and time dependent motion, you can also have time dependent shapes and combine all three. This is all possible within the same basic framework set up above. For example, we can make our star a bit more active, letting it bulge and swirl. Adding another planet and some moons the magic solar system below is created by this gnuplot script.

Solar v4

#### 4. Conclusion

Gnuplot provides a versatile tool for creating animated gifs of your machine learning data and models, or anything else you could imagine. It has an extensive number of options which allow you to tweak each single property of your graph. The ability to perform simple arithmetic within a gnuplot-script further increases the potential.

* Ignore the rather crummy quality of the embedded images. This is an artifact of only having a 300×300 pixel image, the animation at the top of the page has an 1000×1000 pixel resolution and shows a much better quality.

** Of course with enough persistence you may find a way to  get it done…but there are less sadomasochistic ways of doing this 😉

## COVID-19 in de groep?

België staat de laatste dagen zowat op zijn kop ten gevolge van de huidige corona-crisis. De cijfers schieten als een pijl de hoogte in, en geen van de tot nu toe genomen maatregelen lijken het tij te keren. Er wordt duchtig gediscussieerd over het L-woord (lock-down), en het lijkt onoverkomelijk. In plaats van een pleidooi voor of tegen te houden, laat ik je zelf beslissen. De situatie in België is immer zo ernstig dat zelfs primitieve benaderingen de situatie al redelijk goed benaderen. Voor nauwkeurige modellen ben je nog steeds aan het goede adres bij de epidemiologen. Deze modellen kun je gebruiken om strategieën te bedenken om dit virus in te dijken en de situatie op termijn te verbeteren. Dit zijn echter vaak vrij abstracte gegevens. Wat we als gewone burger willen weten is eigenlijk gewoon hoe veilig het voor ons is. Hoe groot is de kans dat we in een groep mensen – op het werk, op school, in de bus, of in de sportclub – één of meerdere personen hebben die met COVID-19 besmet zijn?

Als we aannemen dat besmettingen gelijkmatig verdeeld zijn over het land, leeftijdcategorieën en sociale groepen (dit is niet helemaal het geval, maar door het snel groeiende besmettingsaantal wordt dit een steeds betere benadering) dan kan je heel eenvoudig de kans op een aantal besmette personen “n” in een groep van “N” personen benaderen met de formule:

$kans= \frac{N!}{n!(N-n)!} p^{n}q^{N-n}$

waarbij p de kans is dat een willekeurig persoon besmet is, zijnde de besmettingsgraad. Voor een besmettingsgraad van 500/100 000 (waar alle provincies nu boven zitten) is p=0.005. q is de kans dat een willekeurig persoon niet besmet is (zijnde q=1-p). De term met de uitroeptekens (dat zijn “faculteiten”, en die stellen een reeks van vermenigvuldigingen voor, bijvoorbeeld: 4!=4x3x2x1) vertelt ons op hoeveel manieren (combinaties) er n personen besmet kunnen zijn in een groep van N personen.

Dit is allemaal leuk om weten, maar waar het om draait is natuurlijk wat dit voor jou betekent. Laat ons starten met de situatie van enkele weken geleden, toen er 500 besmettingen per 100 000 werden geconstateerd, dan ziet dat er voor groepen tot 50 personen als volgt uit:

De zwarte lijn toont hoe groot de kans is dat er geen enkele besmette persoon in de groep aanwezig is, terwijl de gekleurde lijnen de kans geven voor exact 1, 2, 3, 4 en 10 personen. Voor een schoolklas met 20-25 leerlingen valt dit nog mee, er is “maar” 10% kans dat er 1 of meerdere besmette leerlingen/leerkracht in de groep zitten. Merk op dat dit voor alle klassen van een school afzonderlijk geldt. Voor een kleine school met 6 jaarklassen (en maar 120-150 mensen) is de kans dat er niemand besmet is reeds gezakt tot ongeveer 50%.

Verdubbelen we de besmettingsgraad naar 1% (of 1 000 per 100 000) dan krijgen we dit beeld:

De kans op minstens één besmet persoon in onze denkbeeldige klas is gestegen tot 20%, terwijl de kans dat de kleine school besmettingsvrij is gebleven is ingezakt tot een magere 20%.

Gaan we naar de situatie zoals deze nu is dan hebben we te maken met een besmettingsgraad van ongeveer 3% (3 000 per 100 000). Het plaatje dat we dan krijgen is het volgende:

De kans op geen enkele besmetting in een klas van 20-25 leerlingen is gezakt naar 1 op 2! De kleine school, daar hoeven we ons geen illusies over te maken: de kans dat daar geen besmettingen zijn is tot nagenoeg nul gezakt.  In een specifieke klas is het intussen ook realistisch geworden dat er meer dan 1 leerling besmet is. Bij deze besmettingsgraad is er zelfs een flinke kans (>10%) op een besmet persoon in een kaartgroep van 4 personen.

#### Deze laatste grafiek is de grafiek van het heden. Dit is de grafiek die je leslokaal, je busrit, je sportvereniging, je kantooromgeving of je werkploeg beschrijft. De keuze is aan ons allemaal om hier onze conclusies uit te trekken. Wachten we op iemand anders om ons te zeggen wat te doen? Of nemen we ons leven en dat van onze familie en vrienden zelf in handen?

Voor wie zelf wil spelen, om bijvoorbeeld de besmettingsgraad van jouw gemeente in te vullen, kan dit met dit excel werkblad, of het online rekenblad (ziet er iets minder mooi uit). Je hoeft enkel de besmettingsgraad aan te passen. De rest gaat vanzelf. Het enige wat je dan nog moet doen, is zelf je conclusies trekken.

## Casting Keynotes: The Virtual Lab

Last Tuesday? I had the pleasure of competing in the casting keynotes competition of the TEDx UHasselt chapter. An evening filled with interesting talks on subjects ranging from the FAIR principles of open-data (by Liebet Peeters)  to the duty not stay silent in the face of “bad ideas” and leading a life of purpose. An interesting presentation was the one by Ann Bessemans on visual prosody to improve reading skills in young children as well as reading experience, more specifically the transfer of non-literal-content, for non-native speakers. There was also time for some humor, with the dangerous life of Tim Biesmans, who suffers from peanut-allergies. For him, death lurks around every corner, even in a first-date’s kiss. During my talk, I traced the evolution of computational research as the third paradigm of scientific discovery, showing you can find computational research in every field, and why it is evolving at its break-neck speed.

During the event, both the public and a jury voted on the best presentation, which would then have to present at the TEDx UHasselt in 2020.

And the Winner is …drum roll… Danny Vanpoucke!

So this story will continue during the 2020 TEDx event at UHasselt, and I hope to see you there 🙂

top: Full action shots of my presentation. Moore’s Law as driving force behind computational research, and pondering the meaning of Artificial Intelligence. Bottom: Yes, I won 🙂

## Start to science-communicate

Today and tomorrow, there is a 2-day summer school on science communication held at the University of Antwerp: Let’s Talk Science! During this summer school there are a large number of workshops to participate in, and lectures to attend, dealing with all aspects of science communication.

Wetenschapsbattle Trophy: Hat made by the children for the contestants of the wetenschapsbattle. Mine has diamonds and computers. 🙂

I was invited to represent Hasselt University (and science communication done by its members) during the plenary panel session starting the summer school. The goal of this plenary session was to share our experiences and thoughts on science communication. The contributions varied from hands-on examples to more abstract presentations of what to keep in mind, including useful tips. The central aim of my presentation was directed at identifying the boundary between science communication and scientific communication. Or more precisely, showing that this border may be more artificial than we are aware of. By showing that everyone’s unique in his/her expertise and discipline, I provided the link between conference presentations and presentations for the general public. I traveled through my history of science communication, starting in the middle: with the Science Battle. An event, I wrote about before, where you are asked to explain your work in 15 minutes to an audience of 6-to 12-year-olds. Then I worked my way back via my blog and contributions to “Ik heb been vraag” (such as: if you drop a penny from the Eiffel tower, will this kill someone on the ground?) to the early beginning of my research: simulating STM images. In the latter case, although I was talking to experts in their field (experimental growth and characterization), their total lack of experience in modelling and quantum mechanical simulations transformed my colleagues into “general public”. This is an important aspect to realize, not only for science communication, but also for scientific communication. As a consequence this also means that most of the tips and tricks applicable to science communication are also applicable to scientific communication.

For example: tell a coherent story. As noted by one of my favorite authors – Terry Pratchett – the human species might have better been called “Pan Narrans”, the storytelling ape. We tell stories and we remember by stories. This is also a means to make your scien(ce/tific) communication more powerful. I told the story of my passion during science explained and my lecture for de Universiteit van Vlaanderen.

A final point I touched is the question of “Why?”. Why should you do science communication? Some may note that is our duty as scientists, since we are payed with taxpayer money. But personally I believe this is not a good incentive. Science communication should originate from your own passion. It should be because you want to, instead of because you have to. If you want to, it is much easier to show you passion, show your interest, and also take the time to do it.

This brought me back to my central theme: Science communication can be simple and small. E.g. projecting simulated STM images on the wall’s of the medieval castle in Ghent (Gravensteen) during a previous edition of the Ghent Light Festival.

Simulated STM of nanowires projected on the Gravensteen (Ghent) during the 2012 Light Festival). Courtesy of Glenn Pollefeyt

## VSC-users day 2019

It is becoming an interesting yearly occurrence: the VSC user day. During this 5th edition, HPC users of the various Flemish universities gather together at the Belgian Royal Academy of Science (KVAB) to present their state-of-the-art work using the Flemish Tier-1 and Tier-2 supercomputers. This is done during a poster-presentation session. This year, I presented my work with regard to vibrational spectra in solids and periodic systems. In contrast to molecules, vibrational spectra in solids are rarely investigated at the quantum mechanical level due to their high cost. I show that imaginary modes are not necessarily a result of structural instabilities, and I present a method for identifying the vibrational spectrum of a defect.

In addition, international speakers discuss recent (r)evolutions in High Performance Computing, and during workshops, the participants are introduced in new topics such as GPU-computing, parallelization, and the VSC Cloud and data platform. The possibilities of GPU were presented by Ehsan, of the VSC, showing extreme speedups of 10x to 100x, strongly depending on the application, the graphics card. It is interesting to see that simple CUDA prama’s can be used to obtain such effects…maybe I should have a go at them for the Hirshfeld and phonon parts of my HIVE code…if they can deal with quadruple precision, and very large arrays. During the presentation of Joost Vandevondele (ETH Zürich) we learned what the future holds with regard to next generation HPC machines. As increasing speed becomes harder and harder to obtain, people are again looking into dedicated hardware systems, a situation akin to the founding days of HPC. Whether this is a situation we should applaud remains to be seen, as it means that we are moving back to codes written for specific machines. This decrease in portability will probably be alleviated by high level scripting languages (such as python), which at the same time result in a significant loss of the initial gain. (Think of the framework approach to modern programming which leads to trivial applications requiring HPC resources to start.)

In addition, this year the HPC-team of the TIER-1 machine is present for a panel discussion, presenting the future of the infrastructure. The machine nearly doubled in size which is great news. Let us hope that in addition for financing hardware, there is also a significant budget considered for a serious extension of a dedicated HPC support team. Running a Tier-1 machine is not something one does as a side-project, but which requires a constant vigilance of a dedicated team to deal with software updates, resulting compatibility issues, conflicting scripts and just hardware and software running haywire because they can.

With this hope, I look toward the future. A future where computational research is steadily are every quickly is becoming common place in the fabric om academic endeavors.

## Universiteit Van Vlaanderen

A bit over 1 month ago, I told you about my adventure at the film studio of “de Universiteit Van Vlaanderen“. Today is the day the movie is officially released. You can find it at the website of de Universiteit Van Vlaanderen: Video. The video is in Dutch as this is a science-communication platform aimed at the local population, presenting the expertise available at our local universities.

In addition to this video, I was asked by Knack magazine to write a piece on the topic presented. As computational research is my central business I wrote a piece on the subject introducing the general public to the topic. The piece can be read here (in Dutch).

And of course, before I forget, this weekend there was also the half-yearly daylight saving exercise with our clocks.[and in Dutch]

## SBDD XXIV: Diamond workshop

The participants to SBDD XXIV of 2019.  (courtesy of Jorne Raymakers, SBDD XXIV secretary)

Last week the 24th edition of the Hasselt diamond workshop took place (this year chaired by Christoph Becher). It’s already the fourth time, since 2016, I have attended this conference, and each year it is a joy to meet up with the familiar faces of the diamond research field. The program was packed, as usual. And this year the NV-center was again predominantly present as the all-purpose quantum defect in diamond. I keep being amazed at how much it is used (although it has a rather low efficiency) and also about how many open question remain with regard to its incorporation during growth. With a little luck, you may read more about this in the future, as it is one of a few dozen ideas and questions I want to investigate.

A very interesting talk was given by Yamaguchi Takahide, who is combining hexagonal-BN and H-terminated diamond for high performance electronic devices. In such a device the h-BN leads to the formation of a 2D hole-gas at the interface (i.e., surface transfer doping), making it interesting for low dimensional applications. (And it of course hints at the opportunities available with other 2D materials.) The most interesting fact, as well as the most mind-boggling to my opinion, was the fact that there was no clear picture of the atomic structure of the interface. But that is probably just me. For experiments, nature tends to make sure everything is alright, while we lowly computational materials artificers need to know where each and every atom belongs. I’ll have to make some time to find out.

A second extremely interesting presentation was given by Anke Krueger (who will be the chair of the 25th edition of SBDD next year), showing of her groups skill at creating fluorine terminated diamond…without getting themselves killed. The surface termination of diamond with fluorine comes with many different hazards, going from mere poisoning, to fire and explosions. The take-home message: “kids don’t try this at home”. Despite all this risky business, a surface coverage of up to 85% was achieved, providing a new surface termination for diamond, with a much stronger trapping of negative charges near the surface, ideal for forming negatively charged NV centers.

On the last day, Rozita Rouzbahani presented our collaboration on the growth of B doped diamond. She studied the impact of growth conditions on the B concentration and growth speed of B doped diamond surfaces. My computational results corroborate her results and presents the atomic scale mechanism resulting in an increased doping concentration upon increased growth speed. I am looking forward to the submission of this nice piece of research.

And now, we wait another year for the next edition of SBDD, the celebratory 25th edition with a focus on diamond surfaces.

## Universiteit Van Vlaanderen: Will we be able to design new materials using our smartphone in the future?

Yesterday, I had the pleasure of giving a lecture for the Universiteit van Vlaanderen, a science communication platform where Flemish academics are asked to answer “a question related to their research“. This question is aimed to be highly clickable and very much simplified. The lecture on the other hand is aimed at a general lay public.

I build my lecture around the topic of materials simulations at the atomic scale. This task ended up being rather challenging, as my computational research has very little direct overlap with the everyday life of the average person. I deal with supercomputers (which these days tend to be bench-marked in terms of smartphone power) and the quantum mechanical simulation of materials at the atomic scale, two other topics which may ring a bell…but only as abstract topics people may have heard of.

Therefor, I crafted a story taking people on a fast ride down the rabbit hole of my work. Starting from the almost divine power of the computational materials scientist over his theoretical sample, over the reality of nano-scale materials in our day-to-day lives, past the relative size of atoms and through the game nature of simulations and the salvation of computational research by grace of Moore’s Law…to the conclusion that in 25 years, we may be designing the next generation of CPU materials on our smartphone instead of a TIER-1 supercomputer. …did I say we went down the rabbit hole?

The television experience itself was very exhilarating for me. Although my actual lecture took only 15 minutes, the entire event took almost a full day. Starting with preparations and a trial run in the afternoon (for me and my 4 colleagues) followed by make-up (to make me look pretty on television 🙂 … or just to reduce my reflectance). In the evening we had a group diner meeting the people who would be in charge of the technical aspects and entertainment of the public. And then it was 19h30. Tensions started to grow. The public entered the studio, and the show was ready to start. Before each lecture, there was a short interview to test sound and light, and introduce us to the public. As the middle presenter, I had the comfortable position not to be the first, so I could get an idea of how things went for my colleagues, and not to be the last, which can really be destructive on your nerves.

#### …and down the rabbit hole we went.

Full periodic table, with all elements presented with their relative size (if known) created for the Universiteit van Vlaanderen lecture.