| Grant number: | 19/04527-0 |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |
| Effective date (Start): | June 01, 2019 |
| Effective date (End): | August 13, 2023 |
| Field of knowledge: | Physical Sciences and Mathematics - Physics - Condensed Matter Physics |
| Principal Investigator: | Adalberto Fazzio |
| Grantee: | Bruno Focassio |
| Host Institution: | Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil |
| Associated research grant: | 17/02317-2 - Interfaces in materials: electronic, magnetic, structural and transport properties, AP.TEM |
| Associated scholarship(s): | 21/12204-6 - Machine learning for two-dimensional materials' properties, BE.EP.DD |
Abstract We call topological quantum states when the wave function has a distinct character that can be specified by a topological invariant, a discrete quantity that remains unchanged under adiabatic transformations. Topological insulators is a class of materials where there is a strong coupling between the momentum and degree of freedom of the spin. Topological insulators (TI) are materials that have an insulating bulk and a surface with protected metallic states, for example, by time reversal, and which are characterized by a topological invariant called Z2. More recently inspired by TI researchers have sought other topological phases with different protections from time-reversal. Crystalline Topological Insulators (TCIs) are topological phases of matter that are protected by crystal symmetry, this including rotations, mirror plane etc. In the case of crystalline topological insulation requires a definition of the Chern number. The TCI search system requires a classification of its topologically distinct band structures within each crystal class, and a complete classification of the TCI does not yet exist and this search is one of our challenges. Through this project we tried to answer some important questions for the area: (i) among the many 2D, dual topological (DTC) and TCI materials already proposed via Machine Learning, how can we classify them via a point group and how can it be connected to its protection?; (ii) how to interfaces with trivial substances can break their protection by symmetry by proximity effect when the substrate interacts via van der Waals?; (iii) the application of these is a field of research well studied, and all of them seek to use the surface in the case of 3D, and the edges in 2D cases. We know that in the case of TIs this is connected to the value of the band gap and the values of spin-orbit coupling. Using the existing database we propose to develop a descriptor for Machine Learning in order to predict the most favorable conditions; (iv) finally, to study access to the Rashba states, these effects persist in topological insulation and are important if we are to manipulate spin. Understanding and controlling spin carriers is of major interest in the field of spintronics. (AU) | |
| News published in Agência FAPESP Newsletter about the scholarship: | |
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