AG Bouaziz
AG BouazizQuantum Materials and Virtual Materials Design
Our research group focuses on quantum materials and virtual materials design, combining first-principles simulations, data-driven materials discovery,and multiscale modelling to discover and engineer materials with novel electronic and magnetic functionalities.
Quantum materials provide a unique platform where strong electronic interactions, spin–orbit coupling, and relativistic effects create emergent phenomena, including unconventional magnetism, topological states, and exotic quantum phases. Understanding and controlling these properties is essential for developing future information technologies beyond conventional computing. Our research aims to bridge fundamental quantum materials physics and technological applications, with particular emphasis on materials for neuromorphic computing and quantum computing.
A central goal of our group is to develop predictive approaches for the virtual design of quantum materials, connecting atomic-scale understanding with device-relevant functionalities. Topological spin textures offer promising routes towards neuromorphic computing due to their stability, high information density, and nonlinear dynamical behaviour, making them attractive building blocks for future energy-efficient information technologies. Complex quantum-material interfaces are key platforms for quantum computing, where disorder,defects, and structural inhomogeneities strongly influence device performance and require large-scale simulations of realistic systems containing thousands of atoms.
Computational Tools and Methodology
Our group employs various computational approaches for the investigation and design of quantum materials:
- First-principles electronic-structure methods, including all-electron approaches based on the Korringa–Kohn–Rostoker (KKR).
- Green's function method and widely used pseudopotential-based simulation packages such as Quantum ESPRESSO.
Links to codes:
https://spirit-code.github.io
https://jukkr.fz-juelich.de - Interpretable machine-learning approaches for data-driven materials discovery and the development of predictive models.
- Atomistic spin dynamics simulations using the Spirit code to investigate the static and dynamic properties of magnetic systems.
By combining these approaches, we aim to connect electronic-scale properties with emergent magnetic and quantum phenomena across different length and time scales.
Funding
The research group is supported by the North Rhine-Westphalia Rückkehrprogramm – Future Computing with funding of 1,236,056.00 EUR and is hosted at the Faculty of Physics, University of Duisburg-Essen.