Machine-Learning assisted computational search for new Li-Ion

The objective of this project is to apply machine-learning algorithms for the assisted computational search of new energy materials. In particular, cathodes for Li-ion batteries and solid-electrolyte interphase systems will be explored.
Development of new high-performance battery materials is an integral component of overcoming the dependence on fossil fuels and ending the energy and climate crisis. Cobalt, the main component in state-of-the-art Li-ion batteries, has already tripled in price in the past few years, and significant further increases are expected with a shift to electric vehicles and battery grid storage. Novel and cheap materials are urgently needed. Materials discovery in chemistry and material science often relies upon trial and error, and thus, proves challenging since a rational design approach is not present. This project tries to tackle this issue while using an assisted computational search approach to find alternative cathode materials.

Faculty Supervisor:

Oleksandr (Alex) Voznyy

Student:

Partner:

University of Cambridge

Discipline:

Physics

Sector:

Education

University:

University of Toronto

Program: