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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

2024

Joel Davidsson, William Stenlund, Abhijith S Parackal, Rickard Armiento, Igor Abrikosov (2024) Na in diamond: high spin defects revealed by the ADAQ high-throughput computational database npj Computational Materials, Vol. 10, Article 109 Continue to DOI
Patrick Lambrix, Rickard Armiento, Huanyu Li, Olaf Hartig, Mina Abd Nikooie Pour, Ying Li (2024) The materials design ontology Semantic Web, Vol. 15, p. 481-515 Continue to DOI
Huanyu Li, Olaf Hartig, Rickard Armiento, Patrick Lambrix (2024) Ontology-based GraphQL server generation for data access and data integration Semantic Web, p. 1-37 Continue to DOI

2023

Oscar Bulancea Lindvall, Joel Davidsson, Rickard Armiento, Igor Abrikosov (2023) Chlorine vacancy in 4H-SiC: An NV-like defect with telecom-wavelength emission Physical Review B, Vol. 108, Article 224106 Continue to DOI
Huanyu Li, Olaf Hartig, Rickard Armiento, Patrick Lambrix (2023) OBG-gen: Ontology-Based GraphQL Server Generation for Data Integration Proceedings of the ISWC 2023 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice: co-located with 22nd International Semantic Web Conference (ISWC 2023)

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
bild på superdatorn Triolith

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

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.

Artificial intelligence reveals new materials

Artificial intelligence is helping researchers in their search for new useful materials. Researchers at LiU have discovered 90 previously unknown materials, which they predict can be created in the laboratory.

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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