Ph.D. student in atomistic modeling of Lithium-ion batteries

Job description: improving the storage capacity of rechargeable batteries belongs to the category of life-changing projects. In Europe, the transport sector is responsible for 25% of the energy consumption and 23% of the CO2 emission. All-electrical vehicles producing zero carbon emission during their use phase could change this paradigm, provided that high-capacity storage units are available. Lithium-ion batteries (LIBs) are becoming serious candidates for such automotive applications, but first, their energy and power densities should be increased, their cost reduced, and their lifetime extended. The key to reach these objectives consists in selecting the best anode and cathode materials that can accommodate high lithium concentrations, support large morphological changes, and provide a good ion diffusivity and electron conductivity. Instead of fabricating dozens of electrode samples, testing, and characterizing them, a simulation tool can be used to more rapidly explore large design spaces and identify the material(s) with the most promising characteristics. The goal of this research project is therefore to apply the density-functional theory (DFT), a first-principles and powerful technique, to simulate lithium-ion battery electrode atom by atom. The focus will be set on their electrical conductivity and on the factors that enhance and deteriorate it. This work will be performed in parallel with another Ph.D. student who will generate electrode structures and extract ion diffusivity from them.

Applicants should have a Diploma or Master degree in electrical engineering, physics, material science, or in a related discipline, good programming skills (Fortran and/or C/C++), interests in physics-based modeling, and knowledge about Li-ion batteries. Experience with density-functional theory tools (VASP, Quantum ESPRESSO, OpenMX, Siesta, CP2K…) is a plus, but is not required.