Research Supervisor Connect
Van der Waals heterostructures, formed by layering two-dimensional (2D) materials, are revolutionizing material science and technology due to their unique properties and the large number of crystals possible from 2D combinations.(1) The analysis of the electronic structure gives important information to understand fundamental properties of this novel class of materials, which is crucial for performance optimization of devices.(2,3)
This project leverages quantum mechanics and artificial intelligence to explore the extensive heterostructure space (millions of novel bilayers), focusing on calculating their functional properties.(4,5) Initially, density functional theory will compute the target property of a selected set of heterostructures. The resultant database will then use to build machine learning models that will efficiently and accurately estimate the target property across all heterostructures. This approach aims to significantly accelerate material screening, expanding the range of potential hybrid materials for the scientific community. The combination of quantum mechanics and machine learning presents an innovative pathway for discovery novel materials.
Co-Supervisor: Marco Fronzi
Email contact: [email protected], [email protected]
References:
1) Novoselov, K. S., et al Science 353.6298 (2016): aac9439.
2) Li, Peicheng, and Zheng-Hong Lu. Small Science 1.1 (2021): 2000015.3)
3) Biswal, Bubunu, et al. Applied Physics Letters 120.9 (2022).
4) Fronzi, Marco, et al. Advanced Intelligent Systems 3.11 (2021): 2100080.
5) Fronzi, Marco, et al. Advanced Theory and Simulations 3.11 (2020): 2000029.
The opportunity ID for this research opportunity is 3675