The Governing Council of the University of Toronto

Published purpose

One of the critical challenges for some potential 2D materials is the lack of stability which requires surface passivation to suppress rapid degradation. AI-aided material design and simulation will accelerate the discovery of potential candidates with preferable physical and chemical properties for 2D materials. This project will use density functional theory (DFT) computational modeling with AI methods to enable the design of new materials, design of new processes for 2D materials passivation and increase exposure and understanding the impact of molecular absorption on 2D materials. AI-predicted band gap, energy levels, optical properties of 2D materials with different surface modification/passivation will identify the best candidates of materials for the energy harvester, sensors and photonics applications. To identify the selective passivation molecular deposition on 2D materials will open a wide window for 2D materials overcome the challenge of stability issue. The developed AI-driven virtual material screening methods can be adopted for further development in the future.