Rickard Armiento

Associate Professor, Head of Unit, Docent

Associate Professor (Senior Lecturer) in Physical Modelling and head of the Materials Design and Informatics unit at Theoretical Physics. I research computation and AI-based methods like machine learning to understand and develop new materials.

Researcher networks and external publication lists

Diva, Google Scholar, ResearcherID: E-1413-2011, Microsoft Academic, ResearchGate, Scopus, Academia.edu, ORCID: 0000-0002-5571-0814

Publications

2026

Ali Sufyan, Brahim Marfoua, J. Andreas Larsson, Erik van Loon, Rickard Armiento (2026)

High-throughput quantification of altermagnetic band splitting Physical Review Materials, Vol. 10, Article 044407 (Article in journal)

https://dx.doi.org/10.1103/mmdm-hrj4

Marian Brännvall, Björn Alling, Rickard Armiento (2026)

Relationship between constraining fields and effective fields in constrained density functional theory calculations for spin dynamics Physical Review B, Vol. 113, Article 064437 (Article in journal)

https://dx.doi.org/10.1103/nwd8-nsk3

2025

Filip Ekström Kelvinius, Oskar Andersson, Abhijith S. Parackal, Dong Qian, Rickard Armiento, Fredrik Lindsten (2025)

WyckoffDiff- A Generative Diffusion Model for Crystal Symmetry Proceedings of the 42nd International Conference on Machine Learning, p. 15130-15147 (Conference paper)

Marian Arale Brännvall, Rickard Armiento, Björn Alling (2025)

Predicting the Curie temperature in substitutionally disordered alloys using a first-principles based model Journal of Magnetism and Magnetic Materials, Vol. 630, Article 173361 (Article in journal)

https://dx.doi.org/10.1016/j.jmmm.2025.173361

Ghulam Abbas Gilani, Oscar Bulancea Lindvall, Joel Davidsson, Rickard Armiento, Igor A. Abrikosov (2025)

Theoretical characterization of NV-like defects in 4H-SiC using ADAQ with SCAN and r2SCAN meta-GGA functionals Applied Physics Letters, Vol. 126, Article 154001 (Article in journal)

https://dx.doi.org/10.1063/5.0252129

Research

2 million formation energies predicted using a machine learning model.

Materials Design and Informatics

The research in the unit of Materials Design and Informatics is aimed at increased understanding of the theory of materials and has applications in the development of new materials

Theory and Modelling

Pushing science forward through mathematical modelling and computer simulations.

Workshop: OMDI 2021

Workshop on Ontologies for Materials-Databases Interoperability (OMDI 2021)

In the field of materials science, the development of data-driven methods calls for a higher degree of standardisation. Welcome to the international workshop from Linkoping University, Sweden, October 5-7, 2021.

News

Three persons in a room with abstract images projected on the walls and floor.

Searching for materials for ultra-fast quantum computers

The number of materials that could be used in the fast quantum computers of the future has increased thanks to Oscar Groppfeldt’s degree project, for which he had access to the university’s supercomputers.

International collaboration lays the foundation for AI for materials

AI is accelerating the development of new materials. Large-scale use and exchange of data on materials is facilitated by a broad international standard. A major international collaboration now presents an extended version of the OPTIMADE standard.

New AI model helps discover previously unknown materials

The “chemical space” can now be mapped on a greater scale thanks to a new AI model developed by a group of researchers at LiU and the University of Cambridge in England. The discovery has been published in the journal Science Advances.

Organisation

Person with transmission electron microscope.

Department of Physics, Chemistry and Biology (IFM)

Excellent undergraduate teaching and research in the areas of biology, chemistry, materials and applied physics and theory and modelling are conducted at this department.