Sven Rogge awarded an ERC Starting Grant STRAINSWITCH

Sven Rogge has been awarded a Starting Grant 'STRAINSWITCH' by the European Research Council (ERC). With this grant, he is poised to establish his research team at the CMM, with a focus on computational materials modelling.

Given enough time, diamonds transform into graphite. Similarly, many materials around us have the intriguing ability to switch between different solid-state phases, thereby changing their macroscopic behavior such as colour, conductivity, and adsorption capacity. While it is straightforward to observe such phase transformations, it remains highly challenging to control under which conditions of temperature, stress, and guest adsorption they take place. In ‘STRAINSWITCH: Strain engineering to design functional 4D polymorphism in nanostructured materials’, Sven and his research team aim to develop computational models that predict how both atomic-level modifications and phase transformations deform the material and, thus, induce strain fields. These strain fields–areas where the material gets stretched or compressed–are key to understanding how atomic-level modifications and phase transformations interact. This could open the door to rationalising nanostructured materials design for sustainable applications in photovoltaic devices or water harvesters, among others.

ERC Starting Grants are designed to fund talented early-career scientists who are ready to work independently and show potential to be a research leader. In their 2023 call, 400 ERC Starting Grants were awarded, including five granted to researchers at Ghent University.

Researchers specialising in developing and applying computational modelling tools for nanostructured materials who wish to be part of Sven’s research team are encouraged to apply through this page.

Multiple ERC-funded Ph.D. and postdoc positions in strain engineering


The Rogge group, embedded within the multidisciplinary Center for Molecular Modeling ( at Ghent University, Belgium, is looking for highly motivated researchers to perform state-of-the-art computational research in functional nanostructured materials design. These positions fit within a recent Starting Grant (StG) STRAINSWITCH awarded to prof Sven M. J. Rogge by the European Research Council (ERC). This grant aims to establish strain engineering as a new in silico approach to designing functional nanostructured materials such as metal-organic frameworks and metal halide perovskites (see, e.g., We especially welcome candidates with a strong track record who are – or may become – eligible to apply for a prestigious Ph.D. fellowship at our national funding agency or wish to prepare for a European fellowship.

More info about STRAINSWITCH and the different research topics within this ERC StG project

It is often easy to observe the ability of polymorphic materials to undergo a phase transition through changes in colour, conductivity, photovoltaic efficiency, or other functional properties. In contrast, it is challenging to control under which external stimuli, such as stress, temperature, or adsorption, these materials switch. Yet, enabling such polymorphic materials design would be a game changer for pressing societal challenges, from access to drinking water to producing green energy. However, this requires a firm understanding of how changing a material’s structure impacts its polymorphism and macroscopic function.

STRAINSWITCH aims to transform polymorphic material design by establishing the strain engineering concept. The central characteristic of this in silico approach is strain: the extent to which a material deforms due to external or internal triggers. On the one hand, external stimuli generate strain, even before they activate a phase transition. On the other hand, spatial disorder in a structure, tuneable from the atom to the device scale, also induces strain which interferes with external strain fields. Our fundamental idea is that it is possible to systematically predict which disorder is needed to ensure polymorphism only occurs under well-defined external triggers by balancing these internal and external strain fields.

Our research will initially focus on four distinct areas. We are recruiting candidates in each area:

  1. Developing the strain engineering workflow and establishing strain fingerprints for bulk disorder;
  2. Developing state-of-the-art interatomic potentials, including machine-learning potentials and coarse-grained force fields, to model long-range and interfacial disorder;
  3. Developing an open-source molecular dynamics algorithm to mimic the influence of general tensorial stress on polymorphic materials;
  4. Developing an in silico nuclear magnetic resonance spectroscopy toolbox to model amorphous structures.

Don't hesitate to contact prof Rogge ( for informal inquiries or more information.

More info about the CMM                        

The CMM groups about 40 researchers from the Faculty of Science and the Faculty of Engineering and Architecture at Ghent University with molecular modelling interests. It is unique in the university as it clusters computational researchers with various backgrounds, from multiple departments and faculties. The CMM aims to model molecules, materials & processes at the nanoscale by bringing together physicists, chemists, and (bio-)engineers while stimulating collaborations across disciplines. This multidisciplinary collaborative mission is the DNA of the CMM and is crucial in achieving scientific excellence in molecular modelling.

The CMM focuses on frontier research in six primary areas: computational material research on the nanoscale, model development, spectroscopy, many-particle physics, chemical kinetics in nanoporous materials, and bio-organic & organic chemistry. Our research is performed within a strong network of partners at Ghent University and at an (inter)national level. To pursue excellence, we strongly stimulate interactions between the various researchers in our team and our vast network of national and international partners. The prospective candidates will join a strongly connected research team and collaborate with national and international academic partners. The research of the CMM is internationally regarded to be at the forefront of its field.

Who are we looking for?

We are looking for highly motivated and creative Ph.D. candidates and postdoctoral researchers with: 

  • (for Ph.D. candidates) a Master’s degree or an international equivalent in physical chemistry, chemical physics, condensed matter physics, statistical physics, theoretical physics, or a related field obtained before your first working day at the CMM. Students obtaining their Master’s degree in the summer of 2024 are also eligible; 
  • (for postdocs) a completed Ph.D. in any of the abovementioned fields obtained before your first working day at the CMM;
  • demonstrated experience with coding (Python, C, etc.) and quantum chemistry software (Gaussian, VASP, CP2K, etc.) or force-field-based simulations is an advantage (for Ph.D. candidates) and a requirement (for postdocs);
  • a strong interest in molecular modelling;
  • excellent research and scientific writing skills;
  • perseverance and an independent, proactive working style;
  • the willingness to look beyond the borders of your discipline and a solid motivation to work in a multidisciplinary team;
  • high-level written and oral English communication skills with the ability to represent the research team effectively internally and externally, including presenting research outcomes at national and international conferences;
  • above all, the ambition to be at the forefront of in silico nanostructured materials design.

What can we offer you?        

A fixed-term contract (4 years for Ph.D. candidates, 18 months for postdoctoral researchers) with an attractive salary. The selected candidates will moreover get the ability to strengthen their CVs within the context of a strongly motivated and multidisciplinary research team and have the ability to contribute to challenging topical research to solve critical societal questions. They will have the opportunity to attend various international conferences and to include research stays in prominent international research teams in this field. Ghent University boasts a strong community that offers a broad range of training and career possibilities for Ph.D. candidates and postdoctoral researchers. The training opportunities focus on research and transferrable skills such as time management, presentation, and leadership skills.

How to apply?

We intend to fill these positions as soon as possible, preferably in January 2024. Complete applications will be considered on receipt, with interviews occurring on a rolling basis until the positions are filled. Interested candidates are requested to prepare the following documents:

  1. the filled out application form (see 2023-09_ApplicationForm_ERC_SR.docx underneath);
  2. a one-page cover letter/motivation letter explaining your interest in these positions and how you fit into the profile;
  3. a curriculum vitae;
  4. copies of the relevant diplomas (Bachelor’s, Master's, and/or Ph.D. certificate) and transcript (certified record of entire enrollment history at educational school), all merged together. Diplomas and transcripts not in Dutch or English should have an official translation in English.

The files should be saved as PDF and named as follows:


In one mail, these documents should be sent to with the subject “Application STRAINSWITCH YourName”.

File 2023-09_ApplicationForm_ERC_SR.docx104.86 KB

Summer 2023 = 4 CMM PhDs

During last summer four of our young researchers successfully defended their PhD. Underneath you find an overview of their research topics.

Congratulations Michael, Ruben, Liesbeth and Alexander!

Michael Freitas Gustavo

New tools for high-dimensional, expensive, black-box global optimization functions applied to ReaxFF parameterizations – Thursday June 29th, 2023

Supervisor: prof. Toon Verstraelen


This work is seized with improving methods for solving high-dimensional, expensive, and black-box (HEB) global optimizations; particularly in the context of ReaxFF parameterization. We broadly discuss the fundamental difficulties of such optimizations and the most recent algorithms in literature to tackle them. We illustrate how the parameterization of scientific empirical models is often an example of HEB optimization. Of particular interest for this work is the reactive force field, ReaxFF, which we discuss in the context of computational chemistry more broadly. Finding good parameters for ReaxFF force fields is an underserved but important area of study. As an improvement, we introduce an optimization framework, GloMPO, which uses a novel management and control structure to guide global optimization routines. We demonstrate that this approach, compared to standard optimizers, is able to identify better minima and makes more efficient use of computational resources. Due to its modular structure, GloMPO can be configured in many different ways and offers several qualitative advantages to practitioners. Next, we present a set of sensitivity analysis tools which identify the most important parameters of a ReaxFF loss function. The methods we use are based on the state-of-the-art Hilbert–Schmidt independence criterion (HSIC). The results allow users to identify insensitive parameters, and greatly reduce the dimensionality of the optimization problem. This allows optimizers to identify good parameter values, faster, and with less overfitting than without sensitivity treatment. Both of our toolkits have been fully integrated into publicly available commercial software. They have been shown to improve ReaxFF parameterizations and are also flexible enough to be applied to other HEB problems.

Ruben Goeminne

Development of accurate and reliable methods for in silico modeling of adsorption in nanoporous materials – Monday July 10th, 2023

Supervisor: prof. Toon Verstraelen


Metal-organic frameworks (MOFs) are a class of materials that have in recent decades attracted widespread scientific interest. When it became clear that these MOFs could maintain a permanent porosity and huge internal surface area, they were quickly identified as ideal materials for gas adsorption and separation applications. Most strikingly, applications such as the capture of carbon dioxide from industrial smokestacks or the production of water from desert air can be made possible by these materials. However, the gigantic structural diversity of these materials and their inherent flexibility complicates the experimental characterization of their adsorption properties. Therefore, in this doctoral research, advanced computational techniques were developed to computationally determine the adsorption properties of these materials. This work was approached in two ways. On the one hand, methods were developed to more accurately describe the interaction of adsorbates with these MOFs; both by improving traditional force fields, and employing machine learning techniques trained on highly accurate quantum mechanical calculations. On the other hand, methods were developed to efficiently predict the remarkable flexibility of these MOFs during gas adsorption.

Liesbeth De Bruecker

Spectroscopic Fingerprint of Electronic Excitations in Nanoporous Frameworks and Transition Metal Complexes – Tuesday August 29th, 2023

Supervisor: prof. Veronique Van Speybroeck


Electronic excitations are located in the ultraviolet and visible part of the electromagnetic spectrum. Using spectroscopy it is possible to study the interactions between electromagnetic waves and matter. However, this experimental technique does not always allow to fully unravel the spectra. To gain more insight, therefore quantum mechanical computational techniques based on density functional theory are used. In this doctoral thesis, the spectroscopic fingerprint of heterogeneous catalysts and transition metal complexes is studied. When looking for environmentally friendly catalysts, the study of electronic excitations can help to find suitable materials for specific applications. The spectroscopic properties of transition metal complexes can provide insight into the nucleation process of nanoporous lattices. This research shows that computer simulations can provide complementary information to experimental work, which ultimately leads to more efficient materials research.

Alexander Hoffman

Unraveling Phase Transformations and Reactivity in Functional Nanostructured Materials Using Computational Vibrational Spectroscopy – Thursday August 31st, 2023

Supervisor: prof. Veronique Van Speybroeck


Realizing sustainable solutions for societal challenges such as energy production, production of chemicals or capturing greenhouse gases requires the development of new materials with adapted functionalities. In that respect, functional nanostructured materials are very interesting, as their material properties, which strongly depend on the structure on an atomic scale, can be tailored to the specific application. Before these new materials can be used in practice, they must first be thoroughly characterized. To this end, a wide range of spectroscopic techniques are available that investigate the structure of materials through their interaction with radiation. The resulting energy spectrum is often complex, which means that the interpretation is not always obvious. To identify the different spectroscopic signals, molecular simulations that make it possible to understand the microscopic structure of the material are often used. In this PhD, such simulations are used to explain vibrational spectra of functional nanostructured materials with the ultimate goal of understanding the origin of phase transformations and chemical reactions.

Congratulations dr. Massimo Bocus!!

On Friday June 23rd, 2023 Massimo Bocus successfully defended his PhD thesis ‘Towards State-of-the-Art Molecular Simulations for an Accurate Modeling of Intricate Zeolite-Catalyzed reactions’. During his PhD research he was supervised by prof. Veronique Van Speybroeck.

Congratulations Massimo!

Summary of the PhD in laymen’s terms

Zeolites are ubiquitous catalysts in the chemical industry, playing a central role in oil refineries but also in newer and sustainable technologies, including biomass conversion and the conversion of CO2 to hydrocarbons. To understand their working mechanism thoroughly and assist experiments, molecular modeling can be used to investigate these crystalline microporous materials. Unfortunately, zeolite-catalyzed reactions are very complex at the atomic scale, where a multitude of factors can change the reaction outcome including (but not limited to) the presence of guest species, defects, active sites distribution and so on. In this thesis, we present a thorough investigation of several zeolite-catalyzed reactions with advanced molecular modeling techniques. We attempt to gradually improve the description of the working catalyst by first using techniques based on molecular dynamics, where the atoms can move around as they would do at realistic reaction temperatures. Additionally, we rely on state-of-the-art machine learning techniques to speed up our otherwise very computationally expensive simulations, showcasing how these allow for unprecedented insights in the reaction under study.

Veronique Van Speybroeck gives Hassel lecture 2023

With the Hassel lecture, the Department of Chemistry from the University of Oslo and the Norwegian Chemical Society (Norsk Kjemisk Selskap, NKS) highlight innovative research by a distinguished invited lecturer. This year, Veronique Van Speybroeck had the honor to head this yearly lecture organized in honor of Nobel Laureate Odd Hassel.

The first day, Thursday May 25th, the Hassel Lecture was targeted at a broad, non-expert audience. Within this talk Veronique illustrated how functional nanostructured materials for sustainable chemistry, nanosensing and clean energy can be modelled at operating conditions, mimicking as close as possible the experimental conditions to enable future technological solutions. She showed some of our recent endeavors to drastically extend accessible length and time scales in molecular modelling using machine learning techniques and molecular dynamics techniques.

The second lecture, Friday May 26th, was addressed to an audience that is more proficient in the area of molecular modeling. After setting the scene of accessible length and time scales in modelling nanostructure materials, Veronique explained how recently the field of Machine Learning Potentials (MLPs) is steadily making its entrance in the field of nanostructured materials. MLPs hold the potential to extend the accessible length and time scales while retaining quantum accuracy. However, the rise of Machine Learning methods does not make fundamental research on quantum mechanical methods to describe the electronic structure problem for challenging problems unnecessary. Indeed, a correctly trained MLP will at best be as accurate as the underlying quantum mechanical data on which it was generated.

Operando modelling functional nanostructured materials for sustainable chemistry, nanosensing and clean energy | Time and place: May 25, 2023 11:15 AM–12:15 PM, Science Library

From quantum mechanics to machine learning: Bridging length and time scales in modeling nanoporous materials at operating conditions | Time and place: May 26, 2023 11:15 AM–12:15 PM, Avogadro, Department of Chemistry

Various vacancies for PhD students available in the group of Van Speybroeck at the Center for Molecular Modeling

In the framework of several recently granted projects, various vacancies at the PhD level are available within the research group of Van Speybroeck at the Center for Molecular Modeling (

A general description of the research performed within the group of Van Speybroeck can be found here

Vacancies are available in the following research domains :

I. Bridging the gap between density functional theory and quantum tensor networks to accurately model strongly correlated nanostructured materials (DFT+TN)

This research is situated in a concerted research action between the Van Speybroeck group at the Center for Molecular Modeling ( and the group of Frank Verstraete at the Quantum Group ( Furthermore external partners at CALTECH and the University of Munich are involved in the project. 

One of the biggest challenges in computational materials science is the accurate property prediction of nanomaterials exhibiting strong electron correlations, where the behavior is dominated by strong interactions. By merging quantum tensor networks (TN) with commonly used density functional theory (DFT) methods, new DFT+TN framework will be developed which will be applied on a series of technologically relevant nanomaterials. Materials of interest are perovskites having optoelectronic and photocatalytic applications, and metal-organic frameworks having sensing applications. 

Within this research action various PhD positions are available, which vary from very fundamental topics -  where tensor networks having crystal symmetries will be developed to be applicable to realistic materials -  to more application oriented subjects - where band structures will be determined for realistic materials from which effective Hamiltonians will be derived that can be solved with tensor networks. Depending on the interest of the PhD applicant, the research can be more oriented towards fundamental or application oriented research. The interested applicant, should have a strong interest in many-body physics and quantum mechanical methods for complex materials. The future PhD students will work on a very challenging new research direction set up between two groups of excellence namely the Quantum Group and the Center for Molecular Modeling. Furthermore the PhD students will have the opportunity to perform research visits at CALTECH and the University of Munich. 

II. Strain to stabilize metal halide PERovSkites: an Integrated effort from fundamentalS to opto-electronic applicaTions (PERsist).

This research is situated in the framework of a running interuniversity research project PERsist with experimental partners of the KULeuven (prof. Hofkens, Roeffaers; and UAntwerpen (prof. Bals, Van Aert, Verbeeck ;

From the application point of view, this project aims to develop novel opto-electronic materials with higher conversion efficiency and lower production costs to be used in medical and security scanners. Metal halide perovskites (MHPs) have emerged as high-performance semiconductors due to their strong absorption and emission in a broad spectral range and their ease of manufacturing. However their integration in opto-electronic devices is hampered by inherent stability issues. Within this project, we explore a new paradigm based on strain field engineering to stabilize the optically active phase under ambient conditions.  Our proof of concept results were published in Science, which has become a highly cited paper ( The project builds on the synergy between leading experts in high-end micro/spectroscopy & modelling of nanomaterials. The materials are synthesized and tested in devices at the group of prof. Hofkens (KULeuven), characterized by high-resolution Electron microscopy at the EMAT group of the University of Antwerp and modeled within the group of Van Speybroeck at the CMM. 

Within the framework of this project, we aim to develop methods to model realistic metal halide perovskites having length scales up to 50 nm. As quantum mechanical calculations are not feasible anymore on these long length scales, we will develop Machine Learning Potentials (MLPs) for complex Metal Halide Perovskites, having defects at the short and mid-range length scale. The MLPs will be used to simulate materials subjected to external strain from the nano- to the micrometer scale and to evaluate their stability of oxygen and water.  Using these tools, it is the aim to unravel the origin of enhanced stability by applying external strain fields and to evaluate in how far the materials remain stable under realistic operating conditions. 

The interested applicant should have a strong interest in technological applications, method development and simulations for real materials. The selected candidate will work in an emerging research field where MLPs are developed for complex materials. Within the CMM, various PhD students are investing strongly in this research area.  A few recent papers from the CMM, in this field are;

The future PhD student will interact very often with the experimental partners who are part of the project.  

III. DEFect-engineering and machine learning MODeling of UiO-66 and its catalytic properties (DEFMOD)

This research is situated within the framework of a recently granted FWO research project between the group of Van Speybroeck (Center for Molecular Modeling) and the group of prof. Van Der Voort (COMOC,

Within this project we aim to synthesize and characterize meticulously controlled defect engineered MOFs, to model and understand these defects from the nano- to the mesoscale with quantum accuracy (up to 50 nm) and to apply these for catalytic applications. From the computational side, we will develop within this project new models to construct defect engineered materials having spatial heterogeneities from the nano to the mesoscale. A close interaction loop will be set up to structurally validate the mesoscopic systems with experimental characterizations at various levels. For these atomistic models, we will develop machine learning potential based on underlying Density functional theory (DFT) training data generated on nanocells having dimensions less than 10 nm, which are representative for the simulation of mesoscaled systems with structural disorder up to 50 nm. The targeted MLPs need to cover the behavior of defective MOFs close and far from equilibrium including reactive events which occur during catalysis. The CMM has recently published some proof of concept papers to generate MLPs for realistic materials see for example:

This research is thus embedded within a grand effort of the research group of Van Speybroeck to model materials at longer length and time scales while maintaining quantum accuracy. 

Diploma requirements

Master’s degree in the field of Physics, Engineering Physics or an equivalent master. Students who will graduate in the coming months can also apply. 

Job Profile

  • You are highly motivated to become an independent researcher and to contribute to important societal problems related to clean energy production, non-fossil based production of chemicals, design of materials for the next generation energy carriers,…
  • You have a strong interest for quantum mechanical methods applied to technological relevant materials.
  • You have a strong interest to develop, implement and apply new physics oriented models in material science.
  • You have a strong academic record showing your potential to become an excellent researcher.
  • You are able to work in a team and have a strong motivation to collaborate with other researchers within both the CMM and our network.
  • We are looking for candidates with a pro-active working style; willingness to look beyond the borders of his/her own discipline and a strong motivation to work in a multidisciplinary team.
  • Experience with quantum chemistry software (Gaussian, VASP, CP2K,…) and coding (Python, C, ...) is an advantage. For students without modelling experience we will provide active training during the first months.

What we offer

  • A PhD position for a period of four years. There will be an evaluation after one year. 
  • You will be embedded in a dynamic environment at the Center for Molecular Modeling and have plenty of opportunities to interact with fellow researchers. You will have many interactions with our partners involved in the projects. You will have access to state-of-the-art computer infrastructure at the Flemish High-Performance Computing Centre (VSC).
  • You will have the opportunity to follow a wide range of training and educational courses part of the Doctoral Schools program of the Ghent University.
  • You will have the ability to participate actively in various international conferences, perform international research stays at the most prominent universities worldwide with whom we collaborate to strengthen your skills.
  • Interested candidates will have the ability to also contribute to education of the CMM by giving exercise classes.
  • Van Speybroeck has a very strong track record in coaching PhD students, under her supervision ca 35 PhD candidates successfully defended their thesis. Most of them found jobs afterwards in industry, academia and have built very strong CVs during their time at the CMM.

How to apply

Interested candidates are requested to prepare the following documents (all in English) and send them to before June 15th, 2023 with ‘[Vacancies Spring 2023] *FirstName*_*LastName*’ as subject :

1. A complete application form. Download this form underneath. -- Saved as pdf and named as follows: 1_AF_*FirstName*_*LastName*

2. A motivation letter, including the preferred research topic. -- Saved as pdf and named as follows: 2_ML_*FirstName*_*LastName*

3. A curriculum vitae. -- Saved as pdf and named as follows: 3_CV_*FirstName*_*LastName*

4. Copies of the relevant diplomas and transcripts (certified record of full enrollment history at educational school). Diplomas and transcripts that are not in Dutch or in English should have an official translation in Dutch or English. -- Saved as pdf and named as follows: 4_DT_*FirstName*_*LastName*

Further information

If you are interested in one of these positions, you may always contact our project officer through for more information or any other question you would have.

File Application form May 2023101.53 KB

Viewpoint in nature reviews materials: “Simulations in the era of exascale computing”

Exascale computers are supercomputers that can perform 1018 floating point operations per second and started coming online in 2022. In Europe the exascale machine JUPITER is due to launch in 2023. However in 2022, LUMI – one of the pan-European pre-exascale supercomputers located in Finland – was ready to be used. It is the fastest supercomputer in Europe and the third fastest globally (the Top500 list published in November 2022). LUMI is also the seventh greenest supercomputer on the planet (the Green500 list published in November 2022). Belgium is one of the LUMI (Large Unified Modern Infrastructure) consortium countries, among Finland, the Czech Republic, Denmark, Estonia, Iceland, Norway, Poland, Sweden and Switzerland (

As supercomputers offer unprecedented opportunities for modelling complex material, five researchers working on different types of materials discuss the most promising directions in computational materials science in a viewpoint recently published in nature reviews materials. Veronique Van Speybroeck was also invited to contribute to this viewpoint. She expects that the exascale computing era might provide an important impetus to further close the gap between experimental observations and molecular modelling. However, many hurdles are yet to be overcome to integrate these very strong computers into the materials modelling ecosystems. You can read this viewpoint by using the following SharedIt link:

Sven geselecteerd als nieuw lid Jonge Academie

© Jonge Academie (2023)

Vanaf 2023 tot 2028 zal CMM-er Sven Rogge de Jonge Academie vervoegen. Sven ambieert om tijdens zijn termijn de aandacht voor onderzoek in al zijn facetten aan te wakkeren, en dit zowel bij de brede bevolking als bij de verschillende beleidsniveaus.

Om dit doel te bereiken, wil Sven verder inzetten op de uitbouw van wetenschapscommunicatie voor de brede onderzoeksgemeenschap. Enerzijds beoogt hij hiermee een meer diverse groep mensen te overtuigen de stap te zetten naar een studie of carrière in de verschillende takken van de wetenschap. Anderzijds zou dit helpen om onderzoekers en de brede maatschappij dichter met elkaar in contact te brengen om zo oplossingen aan te bieden voor toekomstige uitdagingen.

Sven is computationeel materiaalfysicus. Hij ontwikkelt computeralgoritmes om te begrijpen en voorspellen hoe realistische materialen zich gedragen, startende van hun atomaire structuur en de kwantummechanische wetten. Deze modellen laten toe om nieuwe materialen te ontwerpen en zo een antwoord te bieden op prangende duurzaamheidsvraagstukken door bijvoorbeeld broeikasgassen af te vangen of mechanische schokken efficiënt te absorberen.

De Jonge Academie is een interdisciplinaire en interuniversitaire ontmoetingsplaats van jonge toponderzoekers en kunstenaars met een eigen kijk op wetenschap, maatschappij, kunst en beleid. Zij selecteert ieder jaar, na een open oproep, een tiental nieuwe leden. De dertien nieuwe leden voor de termijn 2023-2028 worden plechtig geïnaugureerd op woensdagnamiddag 8 maart 2023 in het Paleis der Academiën in Brussel.

NY walking dinner CMM 2023

Friday January 20th, 2023 we welcomed the CMM family and friends to raise a glass to the new year. We were happy to reinstall an old tradition of meeting each other in person. We took a moment to look back at the past two years, looked forward to a promising and inspiring 2023 and felt thankful for what we have.

At CMM we can count on the strength and ambition of young people. In the past two years we welcomed 18 master students, 13 new researchers, a new ZAP member and a new project manager. To speak for their ambition, it might be mentioned that in 2021 and 2022 five of our researchers were able to receive an FWO fellowship. Although the covid-period was hard for our young researchers, five of them were able to successfully defend their PhD in the past two years and in 2023 we expect eight new PhDs. We are proud that since the very beginning in 2000, the CMM has successfully supported 66 PhD students. In 2023, all of them will be honored on our new PhD picture wall in the hallway.

We believe that the key to our success – both in research and teaching – is bringing together motivated people with an open vision and an eagerness for excellence through collaboration, without forgetting to care for each other. So to the whole CMM family and friends: thank you!

CMM assists in elucidating the capabilities of polymeric and zeolite membranes for gas adsorption

© KU Leuven - Xiaoyu Tan

The efficient separation of CO2 and methane or nitrogen gas poses an important industrial challenge, for instance for the purification of biogas or flue gasses. A new mixed-matrix membrane (MMM) developed by the KU Leuven is shown to outperform all existing polymer-based membranes in terms of CO2—CH4 mixed-gas selectivity and CO2 permeability. The role of the Na-SSZ-39 zeolite incorporated in the polymer membrane was elucidated with the aid of computational modelling performed by the CMM. The results of this collaborative study were recently published in Science.

Zeolites are not only used as catalysts in the petrochemical industry, but their high thermal and chemical stability makes them also ideally suited for other applications. In this work, the Na-SSZ-39 zeolite was embedded as a filler in a polymeric membrane, forming a zeolite-filled mixed-matrix membrane (MMM). In this way, a selective zeolite can be combined with less expensive and more processable polymers. Notwithstanding the challenging problem of obtaining an MMM with a high zeolite loading in combination with a defect-free polymer-zeolite interface and a highly selective zeolite, such a membrane was realized by the group of prof. Vankelecom (cMACS), which outperforms all existing polymer-based membranes and even most zeolite-only membranes.

Molecular insight in the gas adsorption behaviour of the zeolite was provided by the CMM. Using grand canonical Monte Carlo (GCMC) simulations, adsorption isotherms were constructed for unary and binary gas adsorption cases. Because of the CO2-philicity of the zeolite, due to the preferential electrostatic interaction between CO2 and the sodium ions in the zeolite, the methane uptake was observed to drastically reduce in the presence of CO2. Furthermore, quantum mechanical calculations of the diffusion barrier also showed a difference of almost 20 kJ/mol between CO2 and methane, thus confirming the experimentally observed difference in diffusivity selectivity.

CMM authorsAran Lamaire, prof. Veronique Van Speybroeck


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