Abstract:

2D materials have given rise to a fruitful search for emergent quantum properties of matter, based on intercalation of different 2D sheets and magicangle alignments between sheets. The calculation of such properties in complex heterostructures or small-angle twisted-bilayers is hampered by the
cost of simulation, related to the large size of such structures. In this project, we will apply and develop machine-learning techniques that are capable of learning the electronic structure [1]. The goal is the prediction of largestructure properties (band structures, optical spectra, etc.) at minimal
computational cost but retaining ab initio quality and predictive power.
Ultimately, the PhD candidate would build a database of structure-property relationships of these 2D materials and exploit in depth special regimes of twisted bilayer systems.
Candidates with a background in physics or chemistry, with good math and programing skills are sought. We encourage the application of female candidates.
[1] A. Lewis, A. Grisafi, M. Ceriotti, M. Rossi, J. Chem. Theory Comput. 17,
7203–7214 (2021).
Contact person for scientific questions about the project:
[email protected]
[email protected]