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.

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