Tag: conference

E-MRS Spring meeting 2023

In march 2019, Belgium went into COVID-lock down while I attended the yearly diamond conference (SBDD25). Since then, I have been in a bit of a conference lock down myself as well. By visiting the 2023 spring meeting of E-MRS, this lock down has been lifted for international conferences (outside Belgium). Inside Belgium, there was already the DFT-2022 in Brussels, where I was also part of the National Scientific Committee, and of course SBDD26 & SBDD27, which as a diamond researcher you can not miss.

Coming back to Strassbourg for E-MRS brings back some memories, and generated some nice new ones. This year there was a nice Symposium called “Computations for materials – discovery, design and the role of data“[program] which got my full attention. During the first session on AI-accelerated Materials discovery, I had the pleasure to present some of my own work on the Machine Learning of small data sets (cf. papers on the average model, and UV-curable inks). The symposium was nicely coinciding with much of my interest, and showed two (not unexpected, and maybe symposium biased) trends:

  1.  There is an important evolution toward lab-automation and use of robotics (people don’t want to manually build dozens of battery cells or perform hundreds of repetitive synthesis experiments for materials optimization. This shows the future materials scientist, be it a chemist, physicist or engineer will have to become a robotics and/or programming expert as well. This only strengthens me in my vision for our materiomics [NL] students at UHasselt. These skills will be essential for their future scientific career development.
  2. Machine Learning and Artificial Intelligence will play an important role in future materials design. However, we need a better understanding of what we are doing, and not just use any method and accept it as “excellent” because the R² value is high. For now, we can still get away with the latter, but this will not last much longer. It will become more important to have a simple but interpretable model, rather than a complex (over-fitting) Deep Learning Neural Network without understanding of the underlying physics and chemistry. Also here we will have to put in some effort within the materiomics program.


So after an interesting International conference, and making some new contacts…it is time to return home, four more courses need to be prepared from scratch for coming academic year.

SBDD 25 (aka the COVID19 edition)

Last Wednesday, the 25th edition of the Hasselt Diamond workshop started. The central topic of this celebratory edition was focused on surfaces, perfectly suited to present some of my more recent diamond based work.[1][2] Just as the previous years, the program was packed with interesting talks on anything diamond. Phosphorous doped diamond seemed to be the “new thing” this year, but I could be biased, as I was speaking on phosphorous adsorption myself. Due to a cancellation, I found myself being asked on Monday afternoon to present my work as a talk 😎 , on Wednesday morning 😯 . Because I had been a bit too ambitious in my conference abstract, this talk ended up being nicely complementary to my poster.

Poster SBDD 25 conference, Hasselt 2020

Unfortunately, this celebratory edition also fell victim to the COVID-19 crisis. In addition to being the most popular conversation topic—a close second to diamond research—, it also had a very real impact on the conference itself. The COVID-19 crisis resulted in a drop of attendance from 238 people in 2019 to 143 this year.  In addition, the quickly changing situation worldwide lead to last minute cancellations due to travel restrictions. On Thursday evening, the conference site went into lock down. Furthermore, that evening, the Belgian federal government also decided that schools and higher education should be closed, as well as pubs and restaurants, until April 3rd. There was also the urgent request for people to work from home as much as possible. (Consider this a good example of acting NOW aimed at saving people.)

Consider this computational scientist in lock down in his home lab until further notice.

Workshop Machine Learning for Coatings: ML in the Lab (day 5)

On the fifth and final day of the workshop we return to the lab. Our task as a group: optimize our raspberry pink lacquer with regard to hardness, glossiness and chemical resistance.

The four cans of base material made during day 1 of the workshop were mixed to make sure we were all using the same base material (there are already sufficient noise introducing variables present, so any that can be eliminated should be.). Next, each team got a set of recipes generated with the ML algorithm to create. The idea was to parallelise the human part of the process. This would actually also have made for a very interesting exercise to perform in a computer science program. It showed perfectly how bottlenecks are formed and what impact is of serial sections and access/distribution of resources (or is this just in my mind? 😎  ).  After a first round of samples, we already tried to improve the performance of our unit by starting the preparation of the next batch (prefetching 😉 ) while the results of the previous samples were entered into the ML algorithm, and that was run.

At the end of two update rounds, we discussed the results, there were already some clear improvements visible, but a few more rounds would have been needed to get to the best situation. A very interesting aspect to notice during such an exercise, is the difference in the concept of accuracy for the experimental side and the computational side of the story. While the computer easily spits out values in grams with 10 significant digits, at the experimental side of the story it was already extremely hard to get the same amounts with an accuracy of 0.02 gram (the present air currents give larger changes on the scale).

This workshop was a very satisfying experience. I believe I learned most with regard to Machine Learning from the unintentional observation in the lab. Thank you Christian and Kevin!  


Workshop Machine Learning for Coatings: Stochos and DGCN (day 4)

Day 4 of the workshop is again a machine learning centered day. Today we were introduced into the world of Gaussian Processes, and ML approach which is rooted in statistics and models data by looking at the averages of a distribution of functions…it is a function of functions. In contrast to most other ML approaches it is also very well suited for small data sets, which is why I had my eye on them already for quite some time. However, Gaussian Processes are not perfect and interestingly enough, their drawbacks and benefits seem quite complementary with the benefits and drawbacks to neural networks. Deep Gaussian Covariance Networks (DGCN) find their origin in this observation, and were designed with the idea of compensating the drawbacks of both approaches by combining them. The resulting approach is rather powerful and in contrast to any other ML approach: it does not have any hyper-parameter!!

Tomorrow, during the last day of the workshop, we will be using this DGCN to optimize our raspberry pink lacquers.

Workshop Machine Learning for Coatings: First Machine Learning (day 3)

Gartner hype cycle. Courtesy of Kevin Cremanns.

Gartner hype cycle. Courtesy of Kevin Cremanns.

Today the workshop shifted gears a bit. We left the experimental side of the story and moved fully into the world of machine learning. This change went hand-in-hand with a doubling of the number of participants, showing how a hot-topic machine learning really is.  Kevin Cremanns, who is presenting this part of the workshop, started by putting things into perspective a bit, and warned everyone not to hope for magical solutions (ML and AI have their problems), while at the same time presenting some very powerful examples of what is possible. A fun example is the robotic arm learning to flip pancakes:

During the introduction, all the usual suspects of machine learning passed the stage. And although you can read about them in every ML-book, it is nice to hear them discussed by someone who uses them on a daily basis. This mainly because practical details (often omitted in text-books) are also mentioned, helping one to avoid the same mistakes many have made before you. Furthermore, the example codes provided are extremely well documented, making them an interesting source of teaching material (the online manuals for big libraries like sci-kit learn or pandas tend to be too abstract, too big, and too intertwined for new users).

All-in-all a very interesting day. I look forward to tomorrow, as then we will be introduced into the closed source machine learning library developed at the University Hochschule Niederrhein.

Workshop Machine Learning for Coatings: Magical Humans (day 2)

Today was the second day of the machine learning workshop on coatings. After having focused on the components of coatings, today our focus went to characterization and deposition. The set of available characterization techniques is as extensive as the possible components to use. There was, however, one thing which grabbed my attention: “The magical human observer”. Several characterization techniques were presented to heavily rely on the human observer’s opinion and Fingerspitzengefühl.  Sometimes this even came with the suggestion that such a human observer outperforms the numerical results of characterization machinery. This makes me wonder if this isn’t an indication of a poor translation of the human concept to the experiment intended to perform the same characterization. Another important factor to keep in mind when building automation frameworks and machine learning models.

In the afternoon, we again put on our lab coats and goggles. The task of the day: put our raspberry pink lacquer on different substrates and characterize the glossiness (visually) and the pendulum hardness.

Tomorrow the machine learning will kick in.

Workshop Machine Learning for Coatings (day 1)

Today was the first day of school…not only for my son, but for me as well. While he bravely headed for the second grade of primary school, I was en route to the first day of a week-long workshop on Machine Learning and Coatings technology at the Hochschule Niederrhein in Krefeld. A workshop combining both the practical art of creating coating formulations and the magic of simulation, more specifically machine learning.

During my career as a computational materials researcher, I have worked with almost every type of material imaginable (from solids to molecules, including the highly porous things in between called MOFs), and looked into every aspect available, be it configuration (defects , surfaces, mixtures,…) or materials properties (electronic structure, charge transfer, mechanical behavior and spin configurations). But each and every time, I did this from a purely theoretical perspective*. As a result, I have not set foot in a lab (except when looking for a colleague) since 2002 or 2003, so you can imagine my trepidation at the prospect of having to do “real” lab-work during this workshop.

Participating in such a practical session— even such a ridiculously simple and safe one— is a rather interesting experience. The safety-goggles, white-coat and gloves are cool to wear, true, but from my perspective as a computational researcher who wants to automate things, this gives me a better picture of what is going on. For example, we** carefully weigh 225.3 grams of a liquid compound and add 2.2 grams of another (each with an accuracy of about 0.01 gram). In another cup, we collect two dye compounds (powders), again trying our best to perfectly match the prescribed quantities. But when the two are combined in the mixer it is clear that a significant quantity (multiple grams) are lost, just sticking to the edge of the container and spatula. So much for carefully weighing (of course a pro has tricks and skills to deal with this better than we did, but still). Conclusion: (1)Error bars are important, but hard to define. (2) Mixtures made by hand or by a robot should be quite different in this regard.

For the theoretical part of my brain, mixing 10 compounds is just putting them in the same box and stir, mix or shake. Practice can be quite different, especially if you need 225 grams of compound A, and 2.2 grams of compound B. This means that for the experimentalist there is a “natural order” for doing things. This order does not exist at the theoretical side of the spectrum***, where I build my automation and machine learning. This, in addition to the implicit interdependence of combined compounds, gives the high-dimensional space of possible mixtures a rather contorted shape. This gives rise to several questions begging for answers, such as: how important is this order, and can we (ab)use all this to make our search space smaller (but still efficient to sample).

At the end of the day, I learned a lot of interesting things and our team of three ended up with a nice raspberry pink varnish.

Next, day two, where we will characterize our raspberry pink varnish.



* Yes, I do see how strange this may appear for someone whose main research focus is aimed at explaining and predicting experiments. 🙂
** We were divided in teams of 2-3 people, so there were people with actual lab skills nearby to keep me safe. However, if this makes you think I was just idly present in the background, I have to disappoint you. I am brave enough to weigh inanimate powders and slow flowing resins 😉 .
*** Computational research in its practice uses aspects of both the experimental and theoretical branches of research. We think as theoreticians when building models and frameworks, and coax our algorithms to a solution with a gut-feeling and Fingerspitzengefühl only experimentalists can appreciate.

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.

Poster for the VSC user day 2019.

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.

MRS seminar: Topological Insulators

Bart Sorée receives a commemorative frame of the event. Foto courtesy of Rajesh Ramaneti.

Today I have the pleasure of chairing the last symposium of the year of the MRS chapter at UHasselt. During this invited lecture, Bart Sorée (Professor at UAntwerp and KULeuven, and alumnus of my own Alma Mater) will introduce us into the topic of topological insulators.

This topic became unexpectedly a hot topic as it is part of the 2016 Nobel Prize in Physics, awarded last Saturday.

This year’s Nobel prize in physics went to: David J. Thouless (1/2), F. Duncan M. Haldane (1/4) and J. Michael Kosterlitz (1/4) who received it

“for theoretical discoveries of topological phase transitions and topological phases of matter.”

On the Nobel Prize website you can find this document which gives some background on this work and explains what it is. Beware that the explanation is rather technical and at an abstract level. They start with introducing the concept of an order parameter. You may have heard of this in the context of dynamical systems (as I did) or in the context of phase transitions. In the latter context, order parameters are generally zero in one phase, and non-zero in the other. In overly simplified terms, one could say an order parameter is a kind of hidden variable (not to be mistaken for a hidden variable in QM) which becomes visible upon symmetry breaking. An example to explain this concept.

Example: Magnetization of a ferromagnet.

In a ferromagnetic material, the atoms have what is called a spin (imagine it as a small magnetic needle pointing in a specific direction, or a small arrow). At high temperature these spins point randomly in all possible directions, leading to a net zero magnetization (the sum of all the small arrows just lets you run in circles going nowhere). This magnetization is the order parameter. At the high temperature, as there is no preferred direction, the system is invariant under rotation and translations (i.e. if you shift it a bit or you rotate it, or both you will not see a difference) When the temperature is lower, you will cross what is called a critical temperature. Below this temperature all spins will start to align themselves parallel, giving rise to a non-zero magnetization (if all arrows point in the same direction, their sum is a long arrow in that direction). At this point, the system has lost the rotational invariance (because all spins point in  direction, you will know when someone rotated the system) and the symmetry is said to have broken.

Within the context of phase transitions, order parameters are often temperature dependent. In case of topological materials this is not the case. A topological material has a topological order, which means both phases are present at absolute zero (or the temperature you will never reach in any experiment no matter how hard you try) or maybe better without the presence of temperature (this is more the realm of computational materials science, calculations at 0 Kelvin actually mean without temperature as a parameter). So the order parameter in a topological material will not be temperature dependent.

Topological insulators

To complicate things, topological insulators are materials which have a topological order which is not as the one defined above 😯 —yup why would we make it easy 🙄 . It gets even worse, a topological insulator is conducting.

OK, before you run away or loose what is remaining of your sanity. A topological insulator is an insulating material which has surface states which are conducting. In this it is not that different from many other “normal” insulators. What makes it different, is that these surface states are, what is called, symmetry protected. What does this mean?

In a topological insulator with 2 conducting surface states, one will be linked to spin up and one will be linked to spin down (remember the ferromagnetism story of before, now the small arrows belong to the separate electrons and exist only in 2 types: pointing up=spin up, and pointing down=spin down). Each of these surface states will be populated with electrons. One state with electrons having spin up, the other with electrons having spin down. Next, you need to know that these states also have a real-space path let the electrons run around the edge of material. Imagine them as one-way streets for the electrons. Due to symmetry the two states are mirror images of one-another. As such, if electrons in the up-spin state more left, then the ones in the down-spin state move right. We are almost there, no worries there is a clue. Now, where in a normal insulator with surface states the electrons can scatter (bounce and make a U-turn) this is not possible in a topological insulator. But there are roads in two directions you say? Yes, but these are restricted. And up-spin electron cannot be in the down-spin lane and vice versa. As a result, a current going in such a surface state will show extremely little scattering, as it would need to change the spin of the electron as well as it’s spatial motion. This is why it is called symmetry protected.

If there are more states, things get more complicated. But for everyone’s sanity, we will leave it at this.  😎

tUL Life Sciences Research Day 2016

Yesterday was the tUL Life Sciences Research Day 2016. A conference event build around finding collaboration possibilities between the University of Hasselt in Belgium and the University of Maastricht (The Netherlands)…after all tUL is the “transnational University Limburg” which brings two universities together that are only separated some 26 km, but you have to cross a national border.

Although Life sciences itself is not my personal niche, I went to look for opportunities, as nano-particles which are used for drug delivery often consist of metals or oxides. These materials on the other hand are my niche. I used my current work on MOFs as a means to show what is possible from the ab-initio point of view, and presented this as a poster.

tUL Life Science Research Day 2016 Poster

Poster presented at the tUL Life Sciences Research Day, depicting my work on the unfunctionalized and the functionalized MIL-47(V) MOF.