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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

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
Abhijith S Parackal, Rhys E. A. Goodall, Felix A. Faber, Rickard Armiento (2024) Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra Physical Review Materials, Vol. 8, Article 103801 (Article in journal) Continue to DOI
Oscar Bulancea-Lindvall, Joel Davidsson, Ivan G. Ivanov, Adam Gali, Viktor Ivády, Rickard Armiento, Igor A. Abrikosov (2024) Temperature dependence of the AB lines and optical properties of the carbon--antisite-vacancy pair in 4H-SiC Physical Review Applied, Vol. 22, Article 034056 (Article in journal) Continue to DOI
Oskar Andersson, Huanyu Li, Patrick Lambrix, Rickard Armiento (2024) An ontology for units of measures across history,standards, and scientific and technology domains Proceedings of the First International Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science: co-located with the 20th International Conference on Semantic Systems (SEMANTiCS 2024), p. 15-28 (Conference paper)
Matthew L. Evans, Johan Bergsma, Andrius Merkys, Casper W. Andersen, Oskar Andersson, Daniel Beltran, Evgeny Blokhin, Tara M. Boland, Ruben Castaneda Balderas, Kamal Choudhary, Alberto Diaz Diaz, Rodrigo Dominguez Garcia, Hagen Eckert, Kristjan Eimre, Maria Elena Fuentes Montero, Adam M. Krajewski, Jens Jorgen Mortensen, Jose Manuel Napoles Duarte, Jacob Pietryga, Ji Qi, Felipe de Jesus Trejo Carrillo, Antanas Vaitkus, Jusong Yu, Adam Zettel, Pedro Baptista de Castro, Johan Carlsson, Tiago F. T. Cerqueira, Simon Divilov, Hamidreza Hajiyani, Felix Hanke, Kevin Jose, Corey Oses, Janosh Riebesell, Jonathan Schmidt, Donald Winston, Christen Xie, Xiaoyu Yang, Sara Bonella, Silvana Botti, Stefano Curtarolo, Claudia Draxl, Luis Edmundo Fuentes Cobas, Adam Hospital, Zi-Kui Liu, Miguel A. L. Marques, Nicola Marzari, Andrew J. Morris, Shyue Ping Ong, Modesto Orozco, Kristin A. Persson, Kristian S. Thygesen, Chris Wolverton, Markus Scheidgen, Cormac Toher, Gareth J. Conduit, Giovanni Pizzi, Saulius Grazulis, Gian-Marco Rignanese, Rickard Armiento (2024) Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange Digital Discovery (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
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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