Rickard Armiento

Linköping University — Sweden

Materials Science · Physical Sciences

29h-index4.2kcitations158works3.62yr avg
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Research Topics

Machine Learning in Materials Science(46), Advanced Chemical Physics Studies(36), X-ray Diffraction in Crystallography(22), Semiconductor materials and devices(16), Spectroscopy and Quantum Chemical Studies(13)

Publications158 total

Materials-Consortia/optimade-python-tools: v1.4.0
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
Relationship between constraining fields and effective fields in constrained density functional theory calculations for spin dynamics
Physical review. B./Physical review. B·2026Open Access
Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
arXiv (Cornell University)·2026Open Access
Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
ArXiv.org·2026Open Access
Screening 39 billion protostructures for materials discovery
ArXiv.org·2026Open Access
Screening 39 billion protostructures for materials discovery
arXiv (Cornell University)·2026Open Access
High-Throughput Quantification of Altermagnetic Band Splitting
arXiv (Cornell University)·2025Open Access
Predicting the Curie temperature in substitutionally disordered alloys using a first-principles based model
Journal of Magnetism and Magnetic Materials·2025
Theoretical characterization of NV-like defects in 4H-SiC using ADAQ with SCAN and r2SCAN meta-GGA functionals
Applied Physics Letters·2025· 8 citedOpen Access
High-Throughput Exploration of NV-like Color Centers Across Host Materials
arXiv (Cornell University)·2025Open Access
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
arXiv (Cornell University)·2025· 2 citedOpen Access
ADAQ-SYM: Automated symmetry analysis of defect orbitals
Computer Physics Communications·2024· 3 citedOpen Access
Predicting the Curie temperature in substitutionally disordered alloys using a first-principles based model
arXiv (Cornell University)·2024Open Access
Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures
Physical Review Materials·2024· 5 citedOpen Access
Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pretrained machine learning potentials
Physical Review Materials·2024· 8 citedOpen Access
Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra
Physical Review Materials·2024· 5 citedOpen Access
Temperature dependence of the AB lines and optical properties of the carbon–antisite-vacancy pair in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mn>4</mml:mn><mml:mi>H</mml:mi><mml:mtext>−</mml:mtext><mml:mi>Si</mml:mi><mml:mi mathvariant="normal">C</mml:mi></mml:math>
Physical Review Applied·2024· 7 citedOpen Access
Temperature dependence of the AB-lines and Optical Properties of the Carbon-Antisite Vacancy Pair in 4H-SiC
arXiv (Cornell University)·2024Open Access
Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pre-trained machine learning potentials
arXiv (Cornell University)·2024Open Access
Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures
arXiv (Cornell University)·2024· 1 citedOpen Access
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Frequent Co-authors

Ann E. Mattsson(12), Igor A. Abrikosov(10), Stephan Kümmel(8), Joel Davidsson(6), Viktor Ivády(5), Björn Alling(5), Felix A. Faber(4), Alexander Lindmaa(4), Thomas R. Mattsson(4), O. Anatole von Lilienfeld(3), Boris Kozinsky(3), Marco Fornari(3), Gerbrand Ceder(3), Ádám Gali(3), Nuala M. Caffrey(3), Mohammad Amin Gharavi(3), Per Eklund(3), Shyue Ping Ong(2), Maria K. Y. Chan(2), G. Ceder(2)