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.