Tag: artificial intelligence

Dec 16 2025

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of ink formulation Using Phase Doppler Anemometry and Machine Learning

Authors: Pieter Verding, Danny E.P. Vanpoucke, Yunus T. Aksoy, Tobias Corthouts, Maria R. Vetrano, and Wim Deferme
Journal: Adv. Mater. Technol. XX, YY (2025)
doi: 10.1002/admt.202502104
IF(2025): 6.2
export: bibtex
pdf: <AdvMaterTechnol_XX>

 

graphical abstract ML spraycoating
Graphical Abstract: This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D₃₂) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control paving the way for optimized coatings in advanced functional materials.

Abstract

This study examines droplet formation in ultrasonic spray coating (USSC) as a function of ink formulation (solvent, polymer, nanoparticles). First, acetone with polyvinylidene fluoride (PVDF) at concentrations from 0-4.5 wt% is used to examine the effect of polymer additions. Additionally, acetone-based SiO2 nanofluids (0-10 g/L), are explored. Finally, the combination of both polymer (PVDF) and nanoparticles (SiO2) in acetone is studied. Droplet sizes are measured using Phase Doppler Anemometry under varying atomization power and flow rates. Machine Learning (ML) algorithms are employed to develop droplet size models from key spray parameters, including atomization power, flow rate, polymer concentration, and nanoparticle concentration. The model shows significantly higher accuracy than existing empirical models. The model is further validated on IPA-based inks with polyethylenimine (PEIE) or ZnO nanoparticles, and on acetone–cellulose acetate formulations, confirming its robustness across diverse ink systems. In addition to revealing the influence of coating parameters on the droplet formation and distribution, obtained both via experimental validation and ML, this study demonstrates that ML can be effectively applied to small experimental datasets, offering a robust framework for optimizing droplet formation and understanding key spray parameters in USSC for complex, unexplored inks enabling novel coating applications.

Permanent link to this article: https://dannyvanpoucke.be/2025-paper_mldroplets_pieterverding-en/

Jul 25 2025

Special Issue: (R)Evolutions in the Integration of Artificial Intelligence and Machine Learning in Carbon-Based Materials Research

  • Filed under blog

  • July 25, 2025

In light of the ever growing interest in AI and ML within the context of materials research, I’m guest editing a special issue together with Konstantin Klyukin from Auburn University. More information can be found on the flyer below.

(And yes the robot and diamond are AI generated, though it took some effort to get it to have the right number or arms, hold a diamond and look sideways at the same time. 😉

 

 

Permanent link to this article: https://dannyvanpoucke.be/special-issue-revolutions-in-the-integration-of-artificial-intelligence-and-machine-learning-in-carbon-based-materials-research/

Jan 21 2025

Machine Learning and Artificial Intelligence in modern materials science

  • Filed under blog

  • January 21, 2025

One of the most joyful parts of teaching, is when you read a student paper and see their joy of the research shine through.

This year was the second year I taught the course “Machine Learning and Artificial Intelligence in modern materials science“, an elective course in the second master materiomics program. As with my other computational courses, there is a strong hands-on component present in this course: a semester long homework assignment, culminating in a paper and presentation of the work done. The basic idea behind the assignment is simple: Take the QM9 dataset and study it using machine learning and artificial intelligence, incorporating things you learn during the course. In practice this means a lot of coding with for example scikit-learn in combination with using every ounce of physical and chemical intuition they gathered during their previous courses. The absolute freedom generally results in some initial trepidation, but intermediate feedback and the growing understanding that the journey is the the actual goal results in some amazing work.

At the end of the semester, I had three papers before me, which could only be written by these three students (Materiomics is a new program, so having 3 of the 7 students picking a rather hard core computational course is good 😉 ). You could feel their own backgrounds and interests seeping through, as well as the fun they had doing so. There was the engineer who approached the problem from a pipeline perspective, the chemist comparing the efficacy of various fingerprints as features, and the physicist who build a new small fingerprint from scratch creating a linear regression model that outperformed all else having R² =1. The last one is a very nice example of frugal computing, of which we do need more in a world suffering climate change. It was also interesting to see also how three totally different stories also hint at the same underlying properties of the dataset (same target being the hardest to predict), a consistency which provides a level of meta-validation of the results. The students themselves also learned to be critical of their own work by comparing the results of different methods used to attack their own research question.

At the end of this course, it is clear they learned more about artificial intelligence than what is possible by just reading about it. The understood the entire workflow of which training is merely a small part, they learned directly the importance of having good quality data and features, and most importantly they learned that they themselves need to be the I in AI, to be successful…and finally, maybe us four should put our heads together and combine this work into a real research paper, as to celebrate the great research done as a “mere homework-assignment”.

Permanent link to this article: https://dannyvanpoucke.be/machine-learning-and-artificial-intelligence-in-modern-materials-science/

Sep 22 2024

End of summer, and the start of a new academic year.

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  • September 22, 2024

Mid September in Hasselt means the start of a new academic year. It brings to an end a summer of doing some unrestricted research. Especially the last month has been extremely busy.

  • The DFT2024 conference in Paris from August 25th until 30th, where I presented our recent work on the GeV defect, which we will be submitting shortly.
  • The materiomics summer school, where I gave a lecture on performing practical quantum mechanical calculations,
  • The public PhD defense of Emerick Guillaume (QuATOMs group member) on the growth of diamond: congratulations Emerick!
  • A seminar at UNamur on “extreme machine learning”, discussing our work on small datasets and some of the work I did this summer on a spray coating dataset.

Today the first week of academic year ended, and I already had the pleasure teaching quantum mechanics and modelling courses to chemistry and materiomics students. We also welcome 2 MSc materiomics students to the group: Brent Motmans and Eleonora Thomas. Brent will be working on an experimental-theoretical project, where the theoretical side will focus on machine learning of his experimental data. Eleonora on the other hand will be combining DFT and machine learning in her study of diamond. A very warm welcome to the QuATOMs group for both.

 

Permanent link to this article: https://dannyvanpoucke.be/end-of-summer-and-the-start-of-a-new-academic-year/

Dec 22 2023

Materiomics Chronicles: week 13 & 14

Weeks eleven and twelve gave some rest, needed for the last busy week of the semester: week 13. During this week, I have an extra cameo in the first year our materiomics program at UHasselt.

NightCafe's response to the prompt: "Professor teaching quantum chemistry."

NightCafe’s response to the prompt: “Professor teaching quantum chemistry.”

Within the Bachelor of chemistry, the courses introduction to quantum chemistry and quantum and computational chemistry draw to a close, leaving just some last loose to tie up. For the second bachelor students in chemistry, this meant diving into the purely mathematical framework describing the quantum mechanical angular momentum and discovering spin operators are an example, though they do not represent an actual rotating object. Many commutators were calculated and ladder operators were introduced. The third bachelor students in chemistry dove deeper in the quantum chemical modeling of simple molecules, both in theory as well as in computation using a new set of jupyter notebooks during an exercise session.

In the first master materiomics, I had gave the students a short introduction into high-throughput modeling and computational screening approaches during a lecture and exercise class in the course Materials design and synthesis. The students came into contact with materials project via the web-interface and the python API. For the course on Density Functional Theory there was a final guest response lecture, while in the course Machine learning and artificial intelligence in modern materials science a guest lecture on optimal control was provided. During the last response lecture, final questions were addressed.

With week 14 coming to a close, the first semester draws to an end for me. We added another 15h of classes, ~1h of video lecture, and 3h of guest lectures, putting our semester total at 133h of live lectures (excluding guest lectures, obviously). January and February brings the exams for the second quarter and first semester courses.

I wish the students the best of luck with their exams, and I happily look back at surviving this semester.

Permanent link to this article: https://dannyvanpoucke.be/materiomics-chronicles-week-13-14/

Dec 10 2023

Materiomics Chronicles: week 11 & 12

After the exam period in weeks nine and ten, the eleventh and twelfth week of the academic year bring the second quarter of our materiomics program at UHasselt for the first master students. Although I’m not coordinating any courses in this quarter, I do have some teaching duties, including being involved in two of the hands-on projects.

As in the past 10 weeks, the bachelor students in chemistry had lectures for the courses introduction to quantum chemistry and quantum and computational chemistry. For the second bachelor this meant they finally came into contact with the H atom, the first and only system that can be exactly solved using pen and paper quantum chemistry (anything beyond can only be solved given additional approximations.) During the exercise class we investigated the concept of aromatic stabilization in more detail in addition to the usual exercises with simple Schrödinger  equations and wave functions. For the third bachelor, their travel into the world of computational chemistry continued, introducing post-Hartree-Fock methods with also include the missing correlation energy. This is the failure of Hartree-Fock theory, making it a nice framework, but of little practical use for any but the most trivial molecules (e.g. H2 for example already being out of scope). We also started looking into molecular systems, starting with simple diatomic molecules like H2+.

SnV split vacancy defect in diamond.

SnV split vacancy defect in diamond.

In the master materiomics, the course Machine learning and artificial intelligence in modern materials science hosted a guest lecture on Large Language Models, and their use in materials research as well as an exercise session during which the overarching ML study of the QM9 dataset was extended. During the course on  Density Functional Theory there was a second lab, this time on conceptual DFT. For the first master students, the hands-on project kept them busy. One group combining AI and experiments, and a second group combining DFT modeling of SnV0 defects in diamond with their actual lab growth. It was interesting to see the enthusiasm of the students. With only some mild debugging, I was able to get them up and running relatively smoothly on the HPC. I am also truly grateful to our experimental colleagues of the diamond growth group, who bravely set up these experiments and having backup plans for the backup plans.

At the end of week 12, we added another 12h of classes, ~1h of video lecture, ~2h of HPC support for the handson project and 6h of guest lectures, putting our semester total at 118h of live lectures. Upwards and onward to weeks 13 & 14.

Permanent link to this article: https://dannyvanpoucke.be/materiomics-chronicles-week-11-12/

Nov 12 2023

Materiomics Chronicles: week 8

After the complexities of week seven, week eight brings the last lecture week of the first quarter of the academic year. After this week, the students of our materiomics program at UHasselt will start studying for a first batch of exams. It also means with this week, their basic courses come to an end and they have all been brought up to speed and to a similar level, needed for the continuation of their study in the materiomics program.

In the bachelor program, the third bachelor chemistry students ended their detailed study of the He atom in the course quantum and computational chemistry with the investigation of its excited states. They learned about the splitting of in singlet and triplet states as well as Fermi-holes and heaps.

Vulcanoplot

Vulcano-plot of small data model quality of model instances in a large ensemble. Taken from our paper “Small Data Materials Design with Machine Learning: When the Average Model Knows Best“, J. Appl. Phys. 128, 054901 (2020)

The first mater materiomics students got their last lecture in the course Fundamentals of materials modeling, where we looked into some examples of application of machine learning in materials research. We also brought all levels of the course together and imagined how to link these in a multiscale project. Starting from the example of a windmill we discussed the application of computational materials modeling at different scales. For the course Properties of functional materials, the third and final presentation and discussion was held, now focusing on characterization methods. The second master students had response lectures for the courses on Density Functional Theory and Machine learning and artificial intelligence in modern materials science where the various topics of the self study were discussed (e.g., concepts of Neural Networks in case of the latter).

At the end of this week, we have added another 8h of live lectures, putting our semester total at 99h of live lectures. With the workload of the first master materiomics coming to an end, the following chronicles will be biweekly. Upwards and onward to week 9&10.

 

Permanent link to this article: https://dannyvanpoucke.be/materiomics-chronicles-week-8/

Nov 05 2023

Materiomics Chronicles: week 7

After a relatively chill week six, the seventh week of the academic year ended up being complicated. As it was fall-break the week only consisted of two class days at university. However, primary schools are closed entirely so our son was at home having a holiday, while both parents were trying to juggle classes and project proposal deadlines as well as additional administrative reporting. A second evaluation meeting with the students of our materiomics program at UHasselt took place (second master this time), and also these students appreciated the effort put into creating their classes.

Although there were only two days of teaching, this did not mean there was little work there. The students of the second bachelor in chemistry extended their knowledge of a particle in a box to the model of a particle on a ring, during the course introduction to quantum chemistry. Similar as for a particle in a box, this can be considered a simplified model for a circular molecule like benzene, allowing us to estimate the first excitation quite accurately.

For the course Fundamentals of Materials Modeling, there was a lecture introducing the first master students materiomics into the very basics of machine learning, as well as an exercise session. During these, the students learned about linear regression, decision trees and support vector machines. This class was also open to students of the bachelor programs to get a bit of an idea of the content of the materiomics program. Finally, the first master students also presented the results of their lab on finite element modeling as part of the course Fundamentals of Materials Modeling. They presented flow studies around arrows, reef and car models, as well as heat transfer in complex partially insulated systems, as well as sinking boats. They showed they clearly gained insight through this type of hands-on tasks, which is always a joy to note, resulting in grades reflecting their efforts and insights.

Though this week was rather short, we added another 6h of classes, putting our semester total at 91h of live lectures. Upwards and onward to week 8.

Permanent link to this article: https://dannyvanpoucke.be/materiomics-chronicles-week-7/

Oct 22 2023

Materiomics Chronicles: week 5

After week four, this fifth week of the academic year is most arguably the most intense and hectic week of teaching. With 22h of classes and still two classes that needed to be prepared from scratch (even including weekends time was running out), I’m tired but happy it is over. However, all the effort is worth it, and I was happy to hear the students of our materiomics program at UHasselt appreciate the effort put into creating their classes, during an evaluation meeting.

The corral is an artificial structure created from 48 iron atoms (the sharp peaks) on a copper surface. The wave patterns in this scanning tunneling microscope image are formed by copper electrons confined by the iron atoms. Don Eigler and colleagues created this structure in 1993 by using the tip of a low-temperature scanning tunneling microscope (STM) to position iron atoms on a copper surface, creating an electron-trapping barrier. This was the first successful attempt at manipulating individual atoms and led to the development of new techniques for nanoscale construction.source: https://www.nisenet.org/catalog/scientific-image-quantum-corral-top-view

The corral is an artificial structure created from 48 iron atoms (the sharp peaks) on a copper surface. The wave patterns in this scanning tunneling microscope image are formed by copper electrons confined by the iron atoms. Don Eigler and colleagues created this structure in 1993 by using the tip of a low-temperature scanning tunneling microscope (STM) to position iron atoms on a copper surface, creating an electron-trapping barrier. This was the first successful attempt at manipulating individual atoms and led to the development of new techniques for nanoscale construction.
source: https://www.nisenet.org/catalog/scientific-image-quantum-corral-top-view

For the second bachelor students in chemistry the introduction to quantum chemistry finally put them into contact with some “real” quantum mechanics when they were introduced into the world of potential barriers, steps, and wells. Though these are still abstract and toy-model in nature, they provide a first connection with reality, where they can be seen as crude approximations of the potential experienced by valence electrons near the surface, or STM experiments. They were also introduced to my favorite quantum system related to this course: the quantum corral. Without any effort it can be used in half a dozen situations with varying complexity to show and learn basic quantum mechanics. For the third bachelor chemistry students the course quantum and computational chemistry finally provided them the long promised first example of a non-trivial quantum chemical object: The Helium atom. With it’s two electrons, we break free of the H-atom(-like)  world. Using perturbation theory and Slater determinant wave functions, we made our first approximations of its energy. In addition, these students also had a seminar for their course Introductory lectures in preparation to the bachelor project (Kennismakingstraject m.b.t. stage en eindproject, in Dutch). During this lecture I gave a brief introduction and overview of the work I did in the past and the work we do in our research group QuATOMs, which although “quantum” is quite different of what the students experience during their courses on quantum chemistry.

In the materiomics program, the first master students continued their travels into the basics of force-fields during the lecture of the course Fundamentals of materials modelling. The exercise class of this week upped the ante by moving from ASE to LAMMPS for practical modeling of alkane chains, which was also the topic of their second lab session. In the course Properties of functional materials, we investigated the ab initio modelling of vibrations. During the exercise classes we investigated precalculated phonon spectra in the materials-project database, as well as calculated our own vibrational spectrum at the gamma-point of the first Brillouin zone. During the second master course Machine learning and artificial intelligence in modern materials science the central theme was GIGO (Garbage-In-Garbage-Out). How can we make sure our data is suitable and good enough for our models to return useful results. We therefore looked into data-preparation & cleaning, as well as  clustering methods.

At the end of this week, we have added another 22h of live lectures and ~1h of video lectures, putting our semester total at 74h of live lectures. Upwards and onward to week 6.

Permanent link to this article: https://dannyvanpoucke.be/materiomics-chronicles-week-5/

Jul 18 2022

Cover image Polymer International: A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation curable inks

Authors: Danny E.P. Vanpoucke, Marie A.F. Delgove, Jules Stouten, Jurrie Noordijk, Nils De Vos, Kamiel Matthysen, Geert G.P. Deroover, Siamak Mehrkanoon, and Katrien V. Bernaerts
Journal: Polymer International 71(8), i-i (2022)
doi: 10.1002/pi.6434
IF(2015): 3.213
export: bibtex
pdf: <PolymerInt>

Abstract

The cover image is based on the Research Article A machine learning approach for the design of hyperbranched polymeric dispersing agents based on aliphatic polyesters for radiation-curable inks by Danny E.P. Vanpoucke et al., https://doi.org/10.1002/pi.6378.  (The related paper can be found here.)

 

Cover Polymer International: Machine learning on small data sets, application on UV curable inks.

Cover Polymer International: Machine learning on small data sets, application on UV curable inks.

 

Permanent link to this article: https://dannyvanpoucke.be/paper2022_mldannycover-en/