Tag: teaching

Sep 24 2023

Materiomics Chronicles: week 1

The first week of the academic year at UHasselt has come to an end, while colleagues at UGent and KULeuven are still preparing for the start of their academic year next week. Good luck to all of you.

This week started full throttle for me, with classes for each of my six courses. After introductions in classes with new students (for me) in the second bachelor chemistry and first master materiomics, and a general overview in the different courses, we quickly dove into the subject at hand.

The second bachelor students (introduction to quantum chemistry) got a soft introduction into (some of) the historical events leading up to the birth of quantum mechanics such as the black body radiation, the atomic model and the nature of light. They encountered the duck-rabbit of particle-wave duality and awakened their basic math skills with the standing wave problem. For the third bachelor students, the course on quantum and computational chemistry started with a quick recap of the course introduction to quantum mechanics, making sure they are all again up to speed with concepts like braket-notation and commutator relations.

For the master materiomics it was also a busy week. We kicked of the 1st Ma course Fundamentals of materials modeling, which starts of calm and easy with a general picture of the role of computational research as third research paradigm. We discussed in which fields computational research can be found (flabbergasting students with an example in Theology: a collaboration between Sylvia Wenmackers & Helen De Cruz),  approximation vs idealization, examples of materials research at different scales, etc. As a homework assignment the students were introduced into the world of algorithms through the lecture of Hannah Fry (Should computers run the world). For the  2nd Ma, the courses on Density Functional Theory and Machine learning and artificial intelligence in modern materials science both started. The lecture of the former focused on the nuclear wave function and how we (don’t) deal with it in DFT, but still succeed in optimizing structures. During the lecture on AI we dove into the topics of regularization and learning curves, and extended on different types of ensemble models.

At the end of week 1, this brings me to a total of 12h of lectures. Upwards and onward to week 2.

Jun 16 2017

Functional Molecular Modelling: simulating particles in excel

This semester I had several teaching assignments. I was a TA for the course biophysics for the first bachelor biomedical sciences, supervised two 3rd bachelor students physics during their first steps in the realm of computational materials science, and finally, I was responsible for half the course Functional Molecular Modelling for the first Masters Biomedical students (Bioelectronics and Nanotechnology). In this course, I introduce the the students into the basic concepts of classical molecular modelling (quantum modelling is covered by Prof. Wilfried Langenaeker). It starts with a reiteration of some basic concepts from statistics and moves on to cover the canonical ensemble. Things get more interesting with the introduction into Monte-Carlo(MC) and Molecular Dynamics(MD), where I hope to teach the students the basics needed to perform their own MC and MD simulations. This also touches the heart of what this course should cover. If I hear a title like Functional Molecular Modelling, my thoughts move directly to practical applications, developing and implementing models, and performing simulations. This becomes a bit difficult as none of the students have any programming experience or skills.

Luckily there is excel. As the basic algorithms for MC and MD are actually quite simple, this office package can be (ab)used to allow the students to perform very simple simulations. This even without the use of macro’s or any advanced features. Because Excel can also plot the data present in the cells, you immediately see how properties of the simulated system vary during the simulation, and you get direct update of all graphs every time a simulation is run.

It seems I am not the only one who is using excel for MD simulations. In 1995, Fraser and Woodcock even published a paper detailing the use of excel for performing MD simulations on a system of 100 particles. Their MD is a bit more advanced than the setup I used as it made heavy use of macro’s and needed some features to speed things up as much as possible. With the x486 66MHz computers available at that time, the simulations took of the order of hours. Which was impressive, as they served as an example of how computational speed had improved over the years, and compared to the months of supercomputer resources one of the authors had needed 25 years earlier to perform the same thing for his PhD. Nowadays the same excel simulation should only take minutes, while an actual program in Fortran or C may even execute the same thing in a matter of seconds or less.

For the classes and exercises, I made use of a simple 3-atom toy-model with Lennard-Jones interactions. The resulting simulations remain clear allowing their use for educational purposes. In case of  MC simulations, a nice added bonus is the fact that excel updates all its fields automatically when a cell is modified. As a result, all random numbers are regenerated and a new simulation can be performed by saving the excel-sheet or just modifying a not-used cell.

Monte Carlo in excel. A system of three particles on a line, with one particle fixed at 0. All particles interact through a Lennard-Jones potential. The Monte Carlo simulation shows how the particles move toward their equilibrium position.

Monte Carlo in excel. A system of three particles on a line, with one particle fixed at 0. All particles interact through a Lennard-Jones potential. The Monte Carlo simulation shows how the particles move toward their equilibrium position.

The simplicity of Newton’s equations of motion make it possible to perform simple MD simulations, and already for a three particle system, you can see how unstable the algorithm is. Implementation of the leap-frog algorithm isn’t much more complex and shows incredible the stability of this algorithm. In the plot of the total energy you can even see how the algorithm fights back to retain stability (the spikes may seem large, but the same setup with a straight forward implementation of Newton’s equation of motion quickly moves to energies of the order of 100).

Molecular dynamics in excel. A system of three particles on a line, with one particle fixed at 0. All particles interact through a Lennard-Jones potential. The Molecular dynamics simulation shows how the particles move as time evolves. Their positions are updated using the leap-frog algorithm. The extreme hard nature of the Lennard-Jones potential gives rise to the sharp spikes in the total energy. It is this last aspect which causes the straight forward implementation of Newton's equations of motion to fail.

Molecular dynamics in excel. A system of three particles on a line, with one particle fixed at 0. All particles interact through a Lennard-Jones potential. The Molecular dynamics simulation shows how the particles move as time evolves. Their positions are updated using the leap-frog algorithm. The extreme hard nature of the Lennard-Jones potential gives rise to the sharp spikes in the total energy. It is this last aspect which causes the straight forward implementation of Newton’s equations of motion to fail.

 

Jun 09 2017

Bachelor Projects Completed: 2 new computational materials scientists initialised

The black arts of computational materials science.

Black arts of computational materials science.

Just over half a year ago, I mentioned that I presented two computational materials science related projects for the third bachelor physics students at the UHasselt. Both projects ended up being chosen by a bachelor student, so I had the pleasure of guiding two eager young minds in their first steps into the world of computational materials science. They worked very hard, cursed their machine or code (as any good computational scientist should do once in a while, just to make sure that he/she is still at the forefront of science) and survived. They actually did quite a bit more than “just surviving”, they grew as scientists and they grew in self-confidence…given time I believe they may even thrive within this field of research.

One week ago, they presented their results in a final presentation for their classmates and supervisors. The self-confidence of Giel, and the clarity of his story was impressive. Giel has a knack for storytelling in (a true Pan Narrans as Terry Pratchett would praise him). His report included an introduction to various topics of solid state physics and computational materials science in which you never notice how complicated the topic actually is. He just takes you along for the ride, and the story unfolds in a very natural fashion. This shows how well he understands what he is writing about.

This, in no way means his project was simple or easy. Quite soon, at the start of his project Giel actually ran into a previously unknown VASP bug. He had to play with spin-configurations of defects and of course bumped into a hand full of rookie mistakes which he only made once *thumbs-up*. (I could have warned him for them, but I believe people learn more if they bump their heads themselves. This project provided the perfect opportunity to do so in a safe environment. 😎 )  His end report was impressive and his results on the Ge-defect in diamond are of very good quality.

The second project was brought to a successful completion by Asja. This very eager student actually had to learn how to program in fortran before he could even start. He had to implement code to calculate partial phonon densities with the existing HIVE code. Along the way he also discovered some minor bugs (Thank you very much 🙂  ) and crashed into a rather unexpected hard one near the end of the project. For some time, things looked very bleak indeed: the partial density of equivalent atoms was different, and the sum of all partial densities did not sum to the total density. As a result there grew some doubts if it would be possible to even fulfill the goal of the project. Luckily, Asja never gave up and stayed positive, and after half a day of debugging on my part the culprit was found (in my part of the code as well). Fixing this he quickly started torturing his own laptop calculating partial phonon densities of state for Metal-organic frameworks and later-on also the Ge-defect in diamond, with data provided by Giel. Also these results are very promising and will require some further digging, but they will definitely be very interesting.

For me, it has been an interesting experience, and I count myself lucky with these two brave and very committed students. I wish them all the best of luck for the future, and maybe we meet again.