Development of electrolyte descriptors for predicting cycling performance of electrochemical cells
Examensarbete för masterexamen
Systems, control and mechatronics (MPSYS), MSc
This master’s thesis presents the development of structural electrolyte descrip tors utilized for predicting the cycling performance of electrochemical cells such as lithium-ion batteries (LIBs). The research is conducted in collaboration with Compular, a startup company focusing on materials development and battery tech nology improvement. Through molecular dynamics (MD) simulations and trajectory analysis facilitated by Compular’s software, electrolyte descriptors are developed, integrating structural and electrochemical molecular properties. The simulations applied are based on data from battery performance tests, collected and annotated in this project. We propose to use machine learning (ML) to model the relationship between the chemical structure of electrolytes and their performance characteristics. The developed descriptors function as input to a graph neural network (GNN) and thereby offer a novel and efficient method for evaluating electrolyte performance and optimizing electrochemical cells. The findings of this thesis confirm that the descriptors successfully extract necessary information from electrolytes using Com pular’s analysis software, CHAMPION, and demonstrate their compatibility with the GNN. Moreover, the discussion highlights the importance of annotated data, the complexity of electrolyte descriptors and their predictive abilities. Limitations, challenges and potential enhancements are also addressed, underscoring the need for a larger dataset and exploring possible actions to enhance the performance of the model. In conclusion, this research bridges the gap between empirical experi ments and theoretical understanding of battery cycling performance while reducing the need for extensive manual testing. It provides a foundation for further inves tigations into electrolyte performance prediction and represents a significant step towards more efficient and sustainable battery technologies.
lithium-ion batteries, electrolytes, descriptors, machine learning, graph neural network, molecular dynamics