PhD Thesis Defence: Ms. Reshma Devi P (17/11/25)
Thesis title:
Elucidation and prediction of ion transport in battery materials: A first- principles and machine learning study
Faculty advisor(s):
Prof. Sai Gautam Gopalakrishnan
When?
17th November, 2025 (Monday), 10:00 AM (India Standard Time)
Where
KPA Auditorium, Department of Materials Engineering
Abstract
Facile ionic mobility within host frameworks is crucial to the design of high-energy-density batteries with high-power-densities, where the activation energy required for ion migration (Em) between crystallographic sites within electrodes or solid electrolytes, is a governing factor. Em serves as a critical metric for evaluating the commercial viability of battery materials as it directly related to the ionic diffusivity (D(x)) of the intercalants within the host structure, which in turn affects the rate performance of the batteries. Accurate estimation of Em and D(x) using traditional computational methods such as density functional theory (DFT) based nudged elastic band (NEB) simulations or ab-initio molecular dynamics, are typically hindered by several computational challenges, including convergence issues, the demands associated with sampling larger time and length scales, and the scaling of computational cost with system size.