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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

2025

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) Continue to DOI
William Stenlund, Joel Davidsson, Rickard Armiento, Viktor Ivády, Igor Abrikosov (2025) ADAQ-SYM: Automated symmetry analysis of defect orbitals Computer Physics Communications, Vol. 308, Article 109468 (Article in journal) Continue to DOI

2024

Marian Brännvall, Gabriel Persson, Luis Casillas Trujillo, Rickard Armiento, Björn Alling (2024) Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures Physical Review Materials, Vol. 8, Article 114417 (Article in journal) Continue to DOI
Luis Casillas Trujillo, Abhijith S Parackal, Rickard Armiento, Björn Alling (2024) Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pretrained machine learning potentials Physical Review Materials, Vol. 8, Article 113803 (Article in journal) Continue to DOI
Oscar Bulancea Lindvall, Joel Davidsson, Ivan Gueorguiev Ivanov, Adam Gali, Viktor Ivády, Rickard Armiento, Igor Abrikosov (2024) Temperature dependence of the AB lines and optical properties of the carbon-antisite-vacancy pair in 4⁢��−Si⁢C Physical Review Applied, Vol. 22, Article 034056 (Article in journal) Continue to DOI

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.
Two men in a computer server hall.

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.
Rickard Armiento.

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

Department of Physics, Chemistry and Biology (IFM)

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

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