Project Description

This Ph.D. project focuses on the rational design and discovery of next-generation energy storage and conversion materials using integrated multiscale simulation approaches, including Density Functional Theory (DFT), Molecular Dynamics (MD), mechanistic modelling, and AI-driven high-throughput screening techniques. The research will investigate the fundamental structure-property relationships governing ion transport, charge transfer, diffusion pathways, reaction energetics, and material stability in advanced systems such as lithium-ion and solid-state batteries, perovskite materials, solid oxide fuel cells (SOFCs), and other electrochemical energy devices. Atomic-scale simulations will be used to generate key material descriptors and uncover the mechanistic pathways controlling electrochemical performance and durability.

To accelerate materials discovery, the project will integrate machine learning and artificial intelligence approaches with high-throughput computational screening to identify promising materials and catalyst compositions with enhanced electrochemical properties, efficiency, and long-term stability. By combining atomistic simulations with data-driven predictive models across multiple length and time scales, the research aims to establish a comprehensive computational framework for the rapid design and optimization of sustainable, high-performance energy storage and conversion technologies for future clean energy applications.