Predicting physical properties of NMCM cathode materials using machine learning guided DFT simulations
Typ
Examensarbete för masterexamen
Program
Applied physics (MPAPP), MSc
Publicerad
2021
Författare
Stenseke, Alfred
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
With the rapid increase in development of electric vehicles and energy storage systems,
the demand for long lasting batteries with high energy density is higher than
ever before. A crucial aspect of the market-leading lithium battery is the longterm
cycling performance – to perform with high capacity even after thousands of chargedischarge
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-ofthe-
art batteries today use metallic dopants such as aluminum in the cells of the
cathode material. This project investigates other suitable dopants in NCM materials
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.
Beskrivning
Ämne/nyckelord
DFT , NCM , lithium battery , machine learning , prediction