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Abhijith S Parackal

PhD student

Publications

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)

2024

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
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
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 (Article in journal) Continue to DOI

2022

Rhys E. A. Goodall, Abhijith S Parackal, Felix A. Faber, Rickard Armiento, Alpha A. Lee (2022) Rapid discovery of stable materials by coordinate-free coarse graining Science Advances, Vol. 8, Article eabn4117 (Article in journal) Continue to DOI

News

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.

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