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Home » Institute » Departments & Research Groups » Artificial Intelligence for Integrated Material Science

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Department

Artificial Intelligence for Integrated Material Science

Our group works in the development and application of state-of-the-art ab-initio methods to systems of both fundamental and technological interest.


Miguel Marques
Prof. Dr. Miguel Marques

Professor

Room: 00-0107
Tel.: +49 234 32 26370
E-Mail: miguel.marques@rub.de

Research

In present days there is a clear shift towards a new way of doing physics, which relies strongly on the use of computational means. Computational Physics, which is expanding with the availability of modern and more powerful computers, has been offering new insights on various natural phenomena, complementing and going beyond more traditional visions based on analytical approaches.

Our research can be broadly defined as Computational Condensed Matter, sometimes touching the fields of Material Science, Quantum Chemistry, or even Biophysics. Our research activities cover the following areas:

  • Ab-initio structural prediction
  • New materials for photovoltaic applications
  • Spectroscopy of clusters and molecules within time-dependent DFT
  • van der Waals interactions
  • Functionals for (TD)DFT and RDMFT
  • Development of octupus scientific software

Publications of the group can currently be found following the external link below:
https://orcid.org/0000-0003-0170-8222

Members
  • Bispo Da Silva, Thalis
  • Cavignac, Dr.-Ing. Théo
  • De Breuck, Dr.-Ing. Pierre-Paul
  • Klein, Patrick
  • Kowolik, Deborah
  • Loew, Antoine
  • Marques, Prof. Dr. Miguel
  • Mondal, M. Sc. Sumit
  • Pires, M. Sc. Paulo
  • Wang, Haichen
  • Werner, Alina
Recent Publications
  • T. da Silva, T. Cavignac, T. Cerqueira et al. Machine-learning accelerated prediction of two-dimensional conventional superconductors. Materials Horizons, -, -, (2025)
  • A. Aouina, P. Borlido, M. Marques et al. Assessing exchange-correlation functionals for accurate densities of solids. Journal of Chemical Theory and Computation, 20, 10852–10860, (2024)
  • A. Loew, H. Wang, T. Cerqueira et al. Training machine learning interatomic potentials for accurate phonon properties. Machine Learning: Science and Technology, 5, 045019, (2024)
  • J. Schmidt, T. Cerqueira, A. Romero et al. Improving machine-learning models in materials science through large datasets. Materials Today Physics, 48, 101560, (2024)
  • T. Cerqueira, Y. Fang, I. Errea et al. Searching materials space for hydride superconductors at ambient pressure. Advanced Functional Materials, 34, 2404043, (2024)
  • M. Evans, J. Bergsma, A. Merkys et al. Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange. Digital Discovery, 3, 1509–1533, (2024)

All publications

Contact and Office Hours

Department Artificial Intelligence for Integrated Material Science
ICAMS
Ruhr-Universität Bochum
Universitätsstr. 150
44801 Bochum
Germany

Building/Room: ZGH 00-111

E-Mail: miguel.marques@rub.de

PA: Deborah Kowolik
Tel: +49 234 32 17776
E-Mail: deborah.kowolik@rub.de

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Universitätsstraße 150
44801 Bochum

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