Titel of master thesis: Predicting physical properties of NCMM cathode material using machine learning guided DFT simulations
With the rapid increase in development of electric vehicles and energy storage systems over the last decades, the demand for long lasting batteries with high energy density is higher than ever before. One crucial aspect of a lithium battery is the longterm cycling performance -- to perform with high capacity even after thousands of charge-discharge cycles with as small degradation as possible. One cause for this degradation is the occurrence of small micro cracks in the cathode material due to small volume changes during charge-discharge cycles. To suppress this effect, state-of-the-art batteries today use metallic dopants such as aluminum in the cells of the cathode material. This project investigates other suitable dopants by implementing regression and gradient based prediction models on data acquired from supercomputer simulations using density functional theory (DFT). The results, while not fully conclusive, gives indications on what atomic features of dopants are interesting, as well as validates this relatively new machine learning approach in material science.
Student project presentation
Online via Zoom
23 September, 2021, 10:00
23 September, 2021, 11:00