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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: 00/107
Tel.:
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

Members and Publications

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

Members
  • Bispo Da Silva, Thalis
  • Cavignac, Dr.-Ing. Théo
  • De Breuck, Dr.-Ing. Pierre-Paul
  • Fava, Dr. Mauro
  • Guo, Dr. Xiangyu
  • Klein, Patrick
  • Kowolik, Deborah
  • Loew, Antoine
  • Marques, Prof. Dr. Miguel
  • Mondal, M. Sc. Sumit
  • Pires, M. Sc. Paulo
  • Wang, Dr. Haichen
  • Werner, Alina
  • Zehl, Rico
Recent Publications
  • T. Cavignac, J. Schmidt, P. De Breuck et al. AI-driven expansion and application of the Alexandria database. Journal of Physics: Materials, 9, 025014, (2026)
  • P. Pires, T. Bispo Da Silva, K. Gao et al. Machine learning driven exploration of hydride superconductors at ambient pressure. Computational Materials Today, 10, 100052, (2026)
  • K. H. Hiorth, M. Gutierrez-Amigo, T. Cavignac et al. Ab-initio superfluid weight and superconducting penetration depth. Physical Review B, 1, 12, (2026)
  • T. Bispo Da Silva, H. Wang, T. F. T. Cerqueira et al. High-throughput study of electrical conductivity in ordered metals. arXiv:2605.22167, 1, xx, (2026)
  • M. Keller, H. Wang, F. Bechstedt et al. Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation. arXiv:2605.10709, 1, xx, (2026)
  • H. Li, L. Lin, X. Guo et al. Electronic structure engineering in MXene SACs: unveiling the role of mixed termination for ORR/OER bifunctionality. Small, 22, e14330, (2026)

All publications

Groups

Intelligent Materials and Computational Catalysis
 

Dr. Xiangyu Guo
 

Discovery and Characterization of Inorganic Stoichiometric Compounds

Dr. Haichen Wang

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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Ruhr-Universität Bochum
Universitätsstraße 150
44801 Bochum

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