Modeling dendrite growth in Li-ion battery materials

This is a joint PhD position at KU Leuven (Belgium) and Silesian University of Technology (Poland). As a PhD student on this project, you will first work for 2 years at KU Leuven (multi-physics microstructure evolution simulations) and then for 2 years at Silesian University of Technology (attentive generative AI models). Both affiliations will be mentioned on your PhD diploma. Both supervisors (from KU Leuven and Silesian University of Technology) will follow your progress closely during the 4 years and you will have regular project meetings with both supervisors involved to assure the continuity of the PhD research.

At KU Leuven you will work at the Department of Materials Engineering in the research group Nano- and Micro Design of Materials (website: https://www.mtm.kuleuven.be/english/research/scalint/NMDM/nano-microstructure-design-materials)

At Silesian University of Technology, Gliwice, you will work at Faculty of Mechanical Engineering (https://www.polsl.pl/rmt/).

Project

Lithium ion batteries are considered as the materials of the future when it comes to efficient energy storage. One of the remaining problems limiting their lifespan is the formation of lithium dendrites. They are responsible for problems such as short circuits, failures and fires, electrolyte decomposition, and loss of active lithium in these batteries. Dendrite formation is an interfacial process spanning numerous length- and time scales. Despite decades of research, their composition, structure and formation still present a significant conundrum. Achieving completely dendrite-free battery interfaces can only be possible through the correct understanding of the fundamental mechanisms governing dendritic evolution. 

• In the PhD research, you combine multi-physics finite-element simulations with the use of (generative) AI models. Within the multi-physics finite-element model, the morphological evolution of the dendrites due to electrochemical reactions, diffusion, convection and other physical effects will be simulated for two different situations -- liquid and solid electrolytes. The resulting computational datasets will be combined with experimental datasets to train attention mechanism (AM) based generative and/or discriminative machine learning (ML) models (such as MeshGraphNet, variational autoencoder, transformers, ....). 
• The objective is to generate in-silico novel battery architectures or structures that are resistant to dendrite growth. 

Profile

We are looking for 2 PhD students with a strong background in engineering and mathematics and materials science and/or electrochemistry to perform research on the simulation of dendrite growth in Li-ion battery materials. Programming experience is an advantage. 

• As a PhD student, your main task will be to do research in collaboration with other PhD students and researchers. For this position the research work will be mostly numerical and computational work (model development, code implementation, use of finite-element and AI software and analysis of simulation output). You will also have to disseminate your research by writing journal papers, giving presentations on international conferences and participating in outreach activities. 
• At KU Leuven, you will also have to follow a number of technical and general subject classes and you will have some teaching duties, such as guidance of master/bachelor thesis students, guidance of students in lab and exercise sessions or surveillance during exams. 
• At KU Leuven, admission to the doctoral program requires that you have obtained your master diploma with a high score and good English proficiency (IELTS: average of 6.5 or more; TOEFL: average of 79 or more; or equivalent). 

More information on the doctoral program and admission requirements at KU Leuven: https://onderwijsaanbod.kuleuven.be/opleidingen/e/CQ_50045518.htm#activetab=toelatingsvoorwaarden

Offer

Interested?

For more information please contact Prof. dr. ir. Nele Moelans, email: nele.moelans@kuleuven.be, and dr inz. Anil Kunwar, email: anil.kunwar@plsl.pl

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