Tag Archive: computational materials science

Apr 01

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]


Mar 29

Can Europium Atoms form Luminescent Centres in Diamond: A combined Theoretical-Experimental Study

Authors: Danny E. P. Vanpoucke, Shannon S. Nicley, Jorne Raymakers, Wouter Maes, and Ken Haenen
Journal: Diam. Relat. Mater 94, 233-241 (2019)
doi: 10.1016/j.diamond.2019.02.024
IF(2017): 2.232
export: bibtex
pdf: <DiamRelatMater>


Spin polarization around the various Eu-defect models in diamond. Blue and red represent the up and down spin channels respectively
Graphical Abstract: Spin polarization around the various Eu-defect models in diamond. Blue and red represent the up and down spin channels respectively.


The incorporation of Eu into the diamond lattice is investigated in a combined theoretical-experimental study. The large size of the Eu ion induces a strain on the host lattice, which is minimal for the Eu-vacancy complex. The oxidation state of Eu is calculated to be 3+ for all defect models considered. In contrast, the total charge of the defect-complexes is shown to be negative: -1.5 to -2.3 electron. Hybrid-functional electronic-band-structures show the luminescence of the Eu defect to be strongly dependent on the local defect geometry. The 4-coordinated Eu substitutional dopant is the most promising candidate to present the typical Eu3+ luminescence, while the 6-coordinated Eu-vacancy complex is expected not to present any luminescent behaviour. Preliminary experimental results on the treatment of diamond films with Eu-containing precursor indicate the possible incorporation of Eu into diamond films treated by drop-casting. Changes in the PL spectrum, with the main luminescent peak shifting from approximately 614 nm to 611 nm after the growth plasma exposure, and the appearance of a shoulder peak at 625 nm indicate the potential incorporation. Drop-casting treatment with an electronegative polymer material was shown not to be necessary to observe the Eu signature following the plasma exposure, and increased the background

Feb 20

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.

At 21h00, I was up…

and down the rabbit hole we went. 



Full periodic table, with all elements presented with their relative size (if known)

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


Nov 20

Synthesis, characterization and thermodynamic stability of nanostructured Δ-iron carbonitride powder prepared by a solid-state mechanochemical route

Authors: Seyyed Amin Rounaghi, Danny E. P. Vanpoucke, Elaheh Esmaeili, Sergio Scudino, and JĂŒrgen Eckert
Journal: J. Alloys Compd. 778, 327-336 (2019)
doi: 10.1016/j.jallcom.2018.11.007
IF(2017): 3.779
export: bibtex
pdf: <JAlloysCompd>


Nanostructured epsilon iron carbonitride (Δ-Fe3CxN1-x, xâ€ŻâˆŒâ€Ż0.05) powder with high purity (>97 wt%) was synthesized through a simple mechanochemical reaction between metallic iron and melamine. Various characterization techniques were employed to investigate the chemical and physical characteristics of the milling intermediates and the final products. The thermodynamic stability of the different phases in the Fe-C-N ternary system, including nitrogen and carbon doped structures were studied through density functional theory (DFT) calculations. A Boltzmann-distribution model was developed to qualitatively assess the stability and the proportion of the different milling products vs. milling energy. The theoretical and experimental results revealed that the milling products mainly comprise the Δ-Fe3CxN1-xphase with a mean crystallite size of around 15 nm and a trace of amorphous carbonmaterial. The thermal stability and magnetic properties of the milling products were thoroughly investigated. The synthesized Δ-Fe3CxN1-x exhibited thermal stabilities up to 473 K and 673 K in air and argon atmospheres, respectively, and soft magnetic properties with a saturation magnetization of around 125 emu/g.

Nov 13

Predicting Partial Atomic Charges in Siliceous Zeolites

Authors: Jarod J. Wolffis, Danny E. P. Vanpoucke, Amit Sharma, Keith V. Lawler, and Paul M. Forster
Journal: Microporous Mesoporous Mater. 277, 184-196 (2019)
doi: 10.1016/j.micromeso.2018.10.028
IF(2017): 3.649
export: bibtex
pdf: <MicroporousMesoporousMater>


Partial charges in zeolites for force fields.
Graphical Abstract: Partial charges in zeolites for force fields.


Partial atomic charge, which determines the magnitude of the Coulombic non-bonding interaction, represents a critical parameter in molecular mechanics simulations. Partial charges may also be used as a measure of physical properties of the system, i.e. covalency, acidic/catalytic sites, etc. A range of methods, both empirical and ab initio, exist for calculating partial charges in a given solid, and several of them are compared here for siliceous (pure silica) zeolites. The relationships between structure and the predicted partial charge are examined. The predicted partial charges from different methods are also compared with related experimental observations, showing that a few of the methods offer some guidance towards identifying the T-sites most likely to undergo substitution or for proton localization in acidic framework forms. Finally, we show that assigning unique calculated charges to crystallographically unique framework atoms makes an appreciable difference in simulating predicting N2 and O2 adsorption with common dispersion-repulsion parameterizations.

Jul 27

Book chapter: Computational Chemistry Experiment Possibilities

Authors: BartƂomiej M. Szyja and Danny Vanpoucke
Book: Zeolites and Metal-Organic Frameworks, (2018)
Chapter Ch 9, p 235-264
Title Computational Chemistry Experiment Possibilities
ISBN: 978-94-629-8556-8
export: bibtex
pdf: <Amsterdam University Press>


Zeolites and Metal-Organic Frameworks (the hard-copy)


Thanks to a rapid increase in the computational power of modern CPUs, computational methods have become a standard tool for the investigation of physico-chemical phenomena in many areas of chemistry and technology. The area of porous frameworks, such as zeolites, metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs), is not different. Computer simulations make it possible, not only to verify the results of the experiments, but even to predict previously inexistent materials that will present the desired experimental properties. Furthermore, computational research of materials provides the tools necessary to obtain fundamental insight into details that are often not accessible to physical experiments.

The methodology used in these simulations is quite specific because of the special character of the materials themselves. However, within the field of porous frameworks, density functional theory (DFT) and force fields (FF)
are the main actors. These methods form the basis of most computational studies, since they allow the evaluation of the potential energy surface (PES) of the system.


Newsflash: here

Jul 17

Building bridges towards experiments.

Quantum Holy Grail: The Ground-State

Quantum mechanical calculations provide a powerful tool to investigate the world around us. Unfortunately it is also a computationally very expensive tool to use, which puts a boundary on what is possible in terms of computational materials research. For example, when investigating a solid at the quantum mechanical level, you are limited in the number of atoms that you can consider. Even with a powerful supercomputer at hand, a hundred to a thousand atoms are currently accessible for “routine” investigations. The computational cost also limits the number of configurations/combinations you can calculate.

However, in the end— and often with some blood sweat and tears—these calculations do provide you the ground-state structure and energy of your system. From this point forward you can continue characterizing its properties, life is beautiful and happy times are just beyond the horizon. At this horizon your experimental colleague awaits you. And he/she tells you:

Sorry, I don’t find that structure in my sample.

After recovering from the initial shock, you soon realize that in (materials science) experiments one seldom encounters a sample in “the ground-state”. Experiments are performed at temperatures above 0K and pressures above 0 Pa (even in vacuum :p ). Furthermore, synthesis methods often involve elevated temperatures, increased pressure, mechanical forces, chemical reactions,… which give rise to meta-stable configurations. In such an environment, your nicely deduced ground-state may be an exception to the rule. It is only one point within the phase-space of the possible.

So how can you deal with this? You somehow need to sample the phase-space available to the experiment.

Sampling Phase-Space for Ball-milling synthesis.

For a few years now, I have a very fruitful collaboration with Prof. Rounaghi. His interest goes toward the cheap fabrication of metal-nitrides. Our first collaboration focused on AlN, while later work included Ti, V and Cr-nitrides. Although this initial work had a strong focus on simple corroboration through the energies calculated at the quantum mechanical level, the collaboration also allowed me to look at my data in a different way. I wanted to “simulate” the reactions of ball-milling experiments more closely.

Due to the size-limitations of quantum mechanical calculations I played with the following idea:

  • Assume there exists a general master reaction which describes what happens during ball-milling.

X Al + Y Melamine → x1 Al + x2 Melamine + x3 AlN + …

where all the xi represent the fractions of the reaction products present.

  • With the boundary condition that the number of particles needs to be conserved, you end up with a large set of (x1,x2,x3,…) configurations which each have a certain energy. This energy is calculated using the quantum mechanical energies of each product. The configuration with the lowest energy is the ground state configuration. However, investigating the entire accessible phase-space showed that the energies of the other possible configurations are generally not that much higher.
  • What if we used the energy available due to ball-milling in the same fashion as we use kBT? And sample the phase-space using Boltzmann statistics.
  • The resulting Boltzmann distribution of the configurations available in the phase-space can then be used to calculate the mass/atomic fraction of each of the products and allow us to represent an experimental sample as a collection of small units with slightly different configurations, weighted according to their Boltzmann distribution.

This setup allowed me to see the evolution in end-products as function of the initial ratio in case of AlN, and in our current project to indicate the preferred Iron-nitride present.

Grid-sampling vs Monte-Carlo-sampling

Whereas the AlN system was relatively easy to investigate—the phase space was only 3 dimensional— the recent iron based system ended up being 4 dimensional when considering only host materials, and 10 dimensional when including defects. For a small 3-4D phase-space, it is possible to create an equally spaced grid and get converged results using a few million to a billion grid-points. For a 10D phase-space this is no longer possible. As you can no longer keep all data-points (easily) in storage during your calculation (imagine 1 Billion points, requiring you to store 11 double precision floats or about 82Gb) you need a method that does not rely on large arrays of data. For our Boltzmann statistics this gives us a bit of a pickle, as we need to have the global minimum of our phase space. A grid is too course to find it, while a simple Monte-Carlo just keeps hopping around.

Using Metropolis’s improvement of the Monte-Carlo approach was an interesting exercise, as it clearly shows the beauty and simplicity of the approach. This becomes even more awesome the moment you imagine the resources available in those days. I noted 82Gb being a lot, but I do have access to machines with those resources; its just not available on my laptop. In those days MANIAC supercomputers had less than 100 kilobyte of memory.

Although I theoretically no longer need the minimum energy configuration, having access to that information is rather useful. Therefore, I first search the phase-space for this minimum. This is rather tricky using Metropolis Monte Carlo (of course better techniques exist, but I wanted to be a bit lazy), and I found that in the limit of T→0 the algorithm will move toward the minimum. This, however, may require nearly 100 million steps of which >99.9% are rejected. As it only takes about 20 second on a modern laptop…this isn’t a big issue.

Finding a minimum using Metropolis Monte Carlo.

Finding a minimum using Metropolis Monte Carlo.

Next, a similar Metropolis Monte Carlo algorithm can be used to sample the entire phase space. Using 109 sample points was already sufficient to have a nicely converged sampling of the phase space for the problem at hand. Running the calculation for 20 different “ball-milling” energies took less than 2 hours, which is insignificant, when compared to the resources required to calculate the quantum mechanical ground state energies (several years). The figure below shows the distribution of the mass fraction of one of the reaction products as well as the distribution of the energies of the sampled configurations.

Metropolis Monte Carlo distribution of mass fraction and configuration energies for 3 sets of sample points.

Metropolis Monte Carlo distribution of mass fraction and configuration energies for 3 sets of sample points.

This clearly shows us how unique and small the quantum mechanical ground state configuration and its contribution is compared to the remainder of the phase space. So of course the ground state is not found in the experimental sample but that doesn’t mean the calculations are wrong either. Both are right, they just look at reality from a different perspective. The gap between the two can luckily be bridged, if one looks at both sides of the story. 


Jun 07

Science Figured out

Diamond and CPU's, now still separated, but how much longer will this remain the case? Top left: Thin film N-doped diamond on Si (courtesy of Sankaran Kamatchi). Top right: Very old Pentium 1 CPU from 1993 (100MHz), with ”m architecture. Bottom left: more recent intel core CPU (3GHz) of 2006 with nm scale architecture. Bottom right: Piece of single crystal diamond. A possible alternative for silicon, with 20x higher thermal conductivity, and 7x higher mobility of charge carriers.

Diamond and CPU’s, now still separated, but how much longer will this remain the case?
Top left: Thin film N-doped diamond on Si (courtesy of Sankaran Kamatchi). Top right: Very old Pentium 1 CPU from 1993 (100MHz), with ”m architecture. Bottom left: more recent intel core CPU (3GHz) of 2006 with nm scale architecture. Bottom right: Piece of single crystal diamond. A possible alternative for silicon, with 20x higher thermal conductivity, and 7x higher mobility of charge carriers.

Can you pitch your research in 3 minutes, this is the concept behind “wetenschap uitgedokterd/science figured out“. A challenge I accepted after the fun I had at the science-battle. If I can explain my work to a public of 6 to 12 year-olds, explaining it to adults should be possible as well. However, 3 minutes is very short (although some may consider this long in the current bitesize world), especially if you have to explain something far from day-to-day life and can not assume any scientific background.

Where to start? Capture the imagination: “Imagine a world where you are a god.

Link back to the real world. “All modern-day high-tech toys are more and more influenced by the atomic scale details.” Over the last decade, I have seen the nano-scale progress slowly but steadily into the realm of real-life materials research. This almost invisible trend will have a huge impact on materials science in the coming decade, because more and more we will see empirical laws breaking down, and it will become harder and harder to fit trends of materials using a classical mindset, something which has worked marvelously for materials science during the last few centuries. Modern and future materials design (be it solar cells, batteries, CPU’s or even medicine) will have to rely on quantum mechanical intuition and hence quantum mechanical simulations. (Although there is still much denial in that regard.)

Is there a problem to be solved? Yes indeed: “We do not have quantum mechanical intuition by nature, and manipulating atoms is extremely hard in practice and for practical purposes.” Although popular science magazines every so often boast pictures of atomic scale manipulation of atoms and the quantum regime, this makes it far from easy and common inside and outside the university lab. It is amazing how hard these things tend to get (ask your local experimental materials research PhD) and the required blood, sweat and tears are generally not represented in the glory-parade of a scientific publication.

Can you solve this? Euhm…yes…at least to some extend. “Computational materials research can provide the quantum mechanical intuition we human beings lack, and gives us access to atomic scale manipulation of a material.” Although computational materials science is seen by experimentalists as theory, and by theoreticians as experiments, it is neither and both. Computational materials science combines the rigor and control of theory, with access to real-life systems of experiments. It, unfortunately also suffers the limitations of both: as the system is still idealized (but to much lesser extend than in theoretical work) and control is not absolute (you have to follow where the algorithms take you, just as an experimentalist has to follow where the reaction takes him/her). But, if these strengths and weaknesses are balanced wisely (requires quite a few years of experience) an expert will gain fundamental insights in experiments.

Animation representing the buildup of a diamond surface in computational work.

Animation representing the buildup of a diamond surface in computational work.

As a computational materials scientist, you build a real-life system, atom by atom, such that you know exactly where everything is located, and then calculate its properties based on the rules of quantum mechanics, for example. In this sense you have absolute control as in theory. This comes at a cost (conservation of misery 🙂 ); where nature itself makes sure the structure is the “correct one” in experiments, you have to find it yourself in computational work. So you generally end up calculating many possible structural combinations of your atoms to first find out which is the one most probable to represent nature.

So what am I actually doing?I am using atomic scale quantum mechanical computations to investigate the materials my experimental colleagues are studying, going from oxides to defects in diamond.” I know this is vague, but unfortunately, the actual work is technical. Much effort goes into getting the calculations to run in the direction you want them to proceed (This is the experimental side of computational materials science.). The actual goal varies from project to project. Sometimes, we want to find out which material is most stable, and which material is most likely to diffuse into the other, while at other times we want to understand the electronic structure, to test if a defect is really luminescent, this to trace the source of the experimentally observed luminescence. Or if you want to make it more complex, even find out which elements would make diamond grow faster.

Starting from this, I succeeded in creating a 3-minute pitch of my research for Science Figured out. The pitch can be seen here (in Dutch, with English subtitles that can be switched on through the cogwheel in the bottom right corner).

Some external links:


May 22

VSC User Day 2018

Today, I am attending the 4th VSC User Day at the “Paleis de AcademiĂ«n” in Brussels. Flemish researchers for whom the lifeblood of their research flows through the chips of a supercomputer are gathered here to discuss their experiences and present their research.

Some History

About 10 years ago, at the end of 2007 and beginning of 2008, the 5 Flemish universities founded the Flemish Supercomputer Center (VSC). A virtual organisation with one central goal:  Combine their strengths and know-how with regard to High Performance Compute (HPC) centers to make sure they were competitive with comparable HPC centers elsewhere.

By installing a super-fast network between the various university compute centers, each Flemish researcher has nowadays access to state-of-the-art computer infrastructure, independent of his or her physical location. A researcher at the University of Hasselt, like myself, can easily run calculations on the supercomputers installed at the university of Ghent or Leuven. In October 2012 the existing university supercomputers, so-called Tier-2 supercomputers, are joined by the first Flemish Tier-1 supercomputer, which was housed at the brand new data-centre of Ghent University. This machine is significantly larger than the existing Tier-2 machines, and allows Belgium to become the 25th member of the PRACE network, a European network which provides computational researchers access to the best and largest computer facilities in Europe. The fast development of computational research in Flanders and the explosive growth in the number of computational researchers, combined with the first shared Flemish supercomputer (in contrast to the university TIER-2 supercomputers, which some still consider private property rather than part of VSC) show the impact of the virtual organisation that is the VSC. As a result, on January 16th 2014, the first VSC User Day is organised, bringing together HPC users from all 5 universities  and industry. Here the users share their experiences and discuss possible improvements and changes. Since then, the first Tier-1 supercomputer has been decommissioned and replaced by a brand new Tier-1 machine, this time located at the KU Leuven. Furthermore, the Flemish government has put 30M€ aside for super-computing in Flanders, making sure that also in the future Flemish computational research stays competitive. The future of computational research in Flanders looks bright.

Today is User Day 2018

During the 4th VSC User Day, researchers of all 5 Flemish universities will be presenting the work they are performing on the supercomputers of the VSC network. The range of topics is very broad: from first principles materials modelling to chip design, climate modelling and space weather. In addition there will also be several workshops, introducing new users to the VSC and teaching advanced users the finer details of GPU-code and code optimization and parallelization. This later aspect is hugely important during the use of supercomputers in an academic context. Much of the software used is developed or modified by the researchers themselves. And even though this software can present impressive behavior, it doe not speed up automatically if you provide it access to more CPU’s. This is a very non-trivial task the researchers has to take care of, by carefully optimizing and parallelizing his or her code.

To support the researchers in their work, the VSC came up with ingenious poster-prizes. The three best posters will share 2018 node days of calculation time (about 155 years of calculations on a normal simple computer).

Wish me luck!


Single-slide presentation of my poster @VSC User Day 2018.

Single-slide presentation of my poster @VSC User Day 2018.

Jan 19

Newsflash: Book-chapter on MOFs and Zeolites en route to bookstores near you.

It is almost a year ago that I wrote a book-chapter, together with Bartek Szyja, on MOFs and Zeolites. Coming March 2018, the book will be available through University press. It is interesting to note that in a 13 chapter book, ours was the only chapter dealing with the computational study and simulation of these materials…so there is a lot more that can be done by those who are interested and have the patience to perform these delicate and often difficult but extremely rewarding studies. From my time as a MOF researcher I have learned two important things:

  1. Any kind of interesting/extreme/silly physics you can imagine will be present in some MOFs. In this regard, the current state of the MOF/COF field is still in its infancy as most experimental work focuses on  simple applications such as catalysis and gas storage, for which other materials may be better suited. These porous materials may be theoretically interesting for direct industrial application, but the synthesis cost generally will be a bottleneck. Instead, looking toward the fundamental physics applications: Low dimensional magnetism, low dimensional conduction, spin-filters, multiferroics, electron-phonon interactions, interactions between spin and mechanical properties,…. MOFs are a true playground for the theoretician.
  2. MOFs are very hard to simulate correctly, so be wary of all (published) results that come computationally cheap and easy. Although the unit-cell of any MOF is huge, with regard to standard solid state materials, the electron interactions are also quite long range, so the first Brillouin zone needs very accurate sampling (something often neglected). Also spin-configurations can have a huge influence, especially in systems with a rather flat potential energy surface.

In the book-chapter, we discuss some basic techniques used in the computational study of MOFs, COFs, and Zeolites, which will be of interest to researchers starting in the field. We discuss molecular dynamics and Monte Carlo, as well as Density Functional Theory and all its benefits and limitations.

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