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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 (http://molmod.ugent.be).

A general description of the research performed within the group of Van Speybroeck can be found here https://molmod.ugent.be/research-statement-veronique-van-speybroeck

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 (http://molmod.ugent.be) and the group of Frank Verstraete at the Quantum Group (https://quantumghent.github.io/). 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; https://www.hofkenslab.com/) and UAntwerpen (prof. Bals, Van Aert, Verbeeck ; https://www.uantwerpen.be/en/research-groups/emat/)

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 (https://doi.org/10.1126/science.aax3878). 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 https://www.nature.com/articles/s41524-023-00969-x; https://doi.org/10.1021/acs.chemmater.2c01508.

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, https://www.comoc.ugent.be/)

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: https://www.nature.com/articles/s41524-023-00969-x.

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 cmm.vacancies@ugent.be before June 15^th, 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 cmm.vacancies@ugent.be for more information or any other question you would have.

Attachment	Size
Application form May 2023	101.53 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 (https://www.enccb.be/LUMI).

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: https://rdcu.be/c7vRL.

Sven geselecteerd als nieuw lid Jonge Academie

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 20^th, 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

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 CO₂and methane, thus confirming the experimentally observed difference in diffusivity selectivity.

CMM authors – Aran Lamaire, prof. Veronique Van Speybroeck

CMM contributes to the development of more durable materials for solar cells

Solar cells and other optoelectronic devices require materials that efficiently convert light into electron/hole pairs and electrical currents. A collaborative study led by the Roeffaers and Hofkens lab (KU Leuven) led to the engineering of such a material that can operate for several years. The group of prof. Van Speybroeck at CMM contributed to this research and publication with quantum mechanical insights. The results of our study were recently published in Nature Communications.

Metal halide perovskites are promising materials for optoelectronic applications. They absorb sunlight well, the generated electron/hole pairs can diffuse trough large parts of the materials, and they are inexpensive to produce. Among these perovskites, CsPbI₃ continues to receive considerable interest given its superior stability under ambient conditions. The remaining bottleneck is the phase stability of the so-called black phase. This black phase is optoelectronically the most interesting one, but forms spontaneously only at high temperatures. This sought-after phase rapidly degrades at room temperature to a yellow phase with inferior optoelectronic properties. Both the experimental and computational work showed that defining a PbI₂ microgrid hinders this degradation and thus drastically increases the material’s long-term stability.

The laser-defined PbI₂ microgrid separates the CsPbI₃ material into micrometer-sized domains. This created grid acts as an anchoring point for the black phase and prevents the local degradation of one domain from triggering the degradation of neighboring ones. To understand how the microgrid improves the stability of the CsPbI₃ black phase, we performed dynamic quantum mechanical simulations. Although computationally expensive, modeling these quantum mechanical interactions is essential to understand what factors influence the relative phase stabilities of the black and yellow phases. We found that the anchoring on the microgrid inhibits the movements necessary for the transformation from the black to the yellow phase, and thus results in a long-term stable black-phase material usable for optoelectronic applications.

CMM authors – Tom Braeckevelt, dr. Sven Rogge, prof. Veronique Van Speybroeck

Meeting report MOF2022

In September about 700 scientists from 45 countries gathered in Dresden for the 8^th International Conference on Metal-Organic Frameworks and Open Framework Compounds (MOF2022). It was the first physical meeting after the COVID-19 crisis and an ideal platform for scientists, developers and users to reconnect and present their latest findings. Prof. Van Speybroeck and prof. Guy Maurin (CNRS, University of Montpellier) were invited to write the meeting report. Read the full report published in Nature Materials here.

PhD position at CMM for a computational scientist

We are partners in a MSCA DN-JD project (Doctoral Network – Joint Doctorate) SENNET. The aim of SENNET is to create disruptive sensor technology for indoor air quality by incorporating porous materials and sensor technology. The project has pooled the interdisciplinary and intersectoral expertise of leading members located in Belgium, Germany, France, Ireland, Moldova, Spain, the UK and the Netherlands.

We are looking for an excellent PhD student to work on the “characterization of adsorption and dielectric and refractive index response of MOFs and zeolites”. The objective is to accurately predict (1) the adsorption isotherms and selectivity, and (2) the dielectric constants and refractive index response of nanoporous materials towards various volatile organic compounds (VOCs). For this purpose, accurate material specific force fields and/or ab initio trained machine learning potentials for a variety of VOCs need to be constructed to simulate single- and multicomponent isotherms. Additionally, dielectric constants and refractive index responses should be calculated for certain VOCs using ab initio techniques such as density functional theory.

We are looking for a computational scientist with a solid background in atomistic molecular simulations, quantum mechanics, statistical physics and thermodynamics applied to material science.

The candidate should have or will soon obtain a master’s degree of a university or international equivalent in the field of Physics or Physical Engineering. Depending on the curriculum also a master’s in chemistry, chemical engineering or another related field might have the desired background. Experience with molecular simulation software (LAMMPS, DLPOLY, RASPA, Gaussian, VASP, CP2K, …) and coding (Python, C, …) is an advantage. Additionally, the candidate has a pro-active working style, the willingness to look beyond the borders of his/her own discipline and a strong motivation to work in a multidisciplinary team. (S)he is highly motivated to become an independent researcher. We expect a researcher with excellent communication skills and a strong motivation to collaborate with other researchers, within the CMM, the SENNET consortium and our networks.

The selected candidate will not only receive state-of-the-art science/technology training but will also benefit from a unique soft-skills training program within the frame of the MSCA doctoral network, which will kick-start his/her career as a highly employable professional in the EU and beyond. We will host the potential PhD student at Ghent University in our research group, the Center for Molecular Modeling. However, this PhD position will lead to a joint-PhD between Ghent University (CMM) and Université de Montpellier (CNRS). As such a first secondment of six months is planned at CNRS Montpellier under the supervision of prof. Guillaume Maurin. Another secondment of 2-3 months is planned at one of the industrial partners SCM (Software for Chemistry & Materials) for which Stan van Gisbergen will be supervisor.

The successful candidates will receive an attractive salary in accordance with the MSCA regulations for Recruited Researchers. At Ghent University PhD funding is foreseen for 48 months. All applications proceed through the on-line recruitment portal on the https://sennet-project.eu/ website. Please carefully read the eligibility criteria on the website. The initial deadline for on-line registration has been extended for our research position. Ideally the selected PhD student starts prior to March 1^st, 2023.

Potluck breakfast @CMM

After three years we could finally organize our yearly potluck breakfast again. On Thursday morning October 13^th, 2022 we welcomed six new master students and a new PhD student.

At the beginning of October Alen T. Mathew joined CMM to work together with Reza Mehdipour. Alen received his master’s degree in pharmaceutical chemistry from the Indian Institute of Technology. During his master thesis he studied the folding patterns of Tau protein using replica exchange molecular dynamics. At CMM his PhD project is mainly focused on membrane transporters and receptors, which he will study using molecular dynamics simulations and docking.

Hereafter you can find an overview of our new master students and their topics. We wish them and Alen a good time here at CMM and a lot of inspiration upon their scientific journey.

Bjarne De Bruyn - Extracting high-dimensional free energy surfaces from molecular simulations to accurately estimate the rate of physical and chemical processes

Sander De Meyer - The mystery of high-temperature superconductivity: ab-initio calculations using density-functional theory, downfolding and tensor networks

Bram Mornie - From Schrödinger to Newton: ab initio derived classical force fields versus machine learning potentials for zeolite properties

Lisa Ronsyn - Artificial intelligence-driven modeling of protein complexes

Wim Temmerman - Direct CO₂ valorization on zeolite catalysts: Identifying olefin formation pathways in a complex molecular environment

Daan Verraes - Increasing the accuracy of quantum-mechanical simulations for strongly correlated functional materials by designing effective Hamiltonians

Congratulations dr. Maarten Cools-Ceuppens!

On September 15^th our colleague Maarten Cools-Ceuppens successfully defended his PhD. During his defence Maarten presented his work entitled ‘Incorporating long-range interactions and polarization in machine learning potentials with explicit electrons’. He was supervised by prof. dr. ir. Toon Verstraelen and prof. dr. ir. Joni Dambre.

Congratulations Maarten!

Summary of the PhD in laymen's terms

With molecular modeling, one can simulate the behavior of all kinds of materials at the level of individual atoms. At this length scale, systems composed of electrons and nuclei adhere to the laws of quantum mechanics, meaning that they can be simulated by solving the Schrödinger equation. In realistic applications, this approach requires an enormous amount of computing power, which remains challenging, even with today's supercomputers. In practice, one must resort to approximate methods instead. A force field is such a popular approximate method, in which electrons are no longer described explicitly, enabling simulations of larger atomistic systems over longer time scales.

For advanced applications, e.g. those involving charge transfer or polarization, conventional force fields do not capture all the relevant physics. Therefore, in this PhD, we have developed a new force field, in which electrons are re-introduced as semi-classical particles. This force field does capture the physics of charge transfer and polarization, yet without the computational burden of quantum-mechanical models. A machine-learning model was trained to incorporate the complex short-range quantum-mechanical interactions between semi-classical electrons. This new model can indeed learn these interactions automatically and truthfully, which was confirmed by accurate predictions of various electronic properties of organic molecules and periodic atomistic models of solids.

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