News

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!

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

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.

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.