PhD Studentship: (Inter)facing the Bitter Truth: How to Design Better Interfaces in Next-Gen Batteries using Atomistic Simulations Assisted by Machine-Learning

Supervisors:

Bora Karasulu (Chemistry), Albert Bartok-Partay (EngineeringPhysics)

Summary:

Lithium-Sulphur batteries (LSBs) are a promising alternative to Li-ion batteries (LIBs) as a next-gen energy storage technology, providing higher theoretical capacity at lower costs. Replacing the conventional liquid electrolytes with solid electrolytes (SE) helps mitigate the major LSB issues like the Li-polysulfide shuttle effect, and safety risks. Current SEs, however, degrade when coupled with a S-cathode, impeding the Li-ion conduction across their interfaces, limiting the battery performance. To design superior SE/S-cathode interfaces, this project focuses on atomistic simulations of the interfacial sulphide conversion chemistry in LSBs utilising state-of-the-art Density Functional Theory and machine learning methods, providing insights that are otherwise elusive to experimental characterisation techniques.

Background:

Li-S batteries (LSBs) are a promising alternative to Li-ion batteries (LIBs) as a next-gen energy storage technology, owing to their very high theoretical capacity and low cost [1]. In an all-solid-state battery (ASSB), a solid electrolyte (SE) replaces the traditional liquid electrolyte, mitigating the related issues like the shuttle effect of Li-polysulfides, and safety risks. However, various SEs are known to degrade when interfaced with a S-cathode, forming polysulphides that impede the Li-ion conduction across interfaces and the cathode overpotential, limiting the battery performance. To design superior SE-cathode interfaces, the interfacial sulphide conversion chemistry must be known, but experimental characterisation using tools like SEM and TEM is challenging due to the volatility of sulphur [2], rendering atomistic simulations a viable recourse.

Ab initio (DFT) methods are routinely used to discover and characterise bulk ASSB materials, but their applications in modelling interfaces are rather limited, mainly due to the much higher computational costs [3]. Larger models are needed to simulate interfaces that adequately retain the bulk properties and minimise the artificial lattice strain between the two surfaces. Also, longer simulations (>100ps) are vital in sampling the Li dendrite growth and polysulphide formation processes. Scaling-up requires ML interatomic potentials (MLIP), that provide near-DFT accuracies at a fraction of DFT costs.

This project therefore focuses on atomistic simulations of the SE-cathode interfaces within LSBs under charging/discharging conditions, representing changes in the chemical states and bonding of the particles. The presence of particles whose oxidation state changes during the simulations requires explicit treatment of electrostatics within the MLIP framework, calling for the extension of current ML models. Therefore, in the project the PhD student will also develop novel MLIP frameworks.

[1] Energy Fuels 2020, 34, 10, 11942–61; [2] Power Sources 2016, 319, 247– 54; [3] Prog. Energy 2022, 4 012002

For further details about the project and how it links to the training included in the HetSys PhD programme, please visit:

(Inter)facing the Bitter Truth: How to Design Better Interfaces in Next-Gen Batteries using Atomistic Simulations Assisted by Machine-Learning (warwick.ac.uk)

Funding

Awards for both UK residents and international applicants pay a stipend to cover maintenance as well as paying the university fees and a research training support. The stipend is at the standard UKRI rate. For more details visit: https://warwick.ac.uk/fac/sci/hetsys/apply/funding/

If you’re from outside the UK, the final application deadline for all courses starting in September/October is 23:59 (GMT) on 25 January 2023.