Date of Award
Spring 2026
Document Type
Open Access Thesis
Department
Chemistry and Biochemistry
First Advisor
Christopher Sutton
Second Advisor
Vitaly Rassolov
Abstract
Aqueous Zn-ion batteries (AZIBs) have emerged as a promising energy storage tech- nology owing to their high Coulombic efciency and low environmental impact. However, their practical development is impeded by limited cycling stability, pri- marily caused by parasitic side reactions occurring at the anode surface. These side reactions are strongly infuenced by the bulk solvation structure of Zn-based elec- trolytes, which remains challenging to characterize using conventional experimental techniques. Machine-learning interatomic potentials (MLIPs) provide a powerful computational approach to investigate these systems and can be employed to eluci- date the bulk structural properties of Zn electrolytes.
Rights
© 2026, Ria Vasdev
Recommended Citation
Vasdev, R.(2026). The Use of Machine-Learning Interatomic Potentials to Study Bulk Properties of Aqueous Zn Ion Batteries. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/8722