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Abhijith S Parackal

Research Engineer

Publications

2026

Abhijith S Parackal, Rickard Armiento, Florian Trybel (2026) Symmetry-Restricted Energy Landscapes as a Benchmark for Machine Learned Interatomic Potentials Advanced Theory and Simulations, Vol. 9, Article e70506 (Article in journal) https://dx.doi.org/10.1002/adts.70506
Abhijith S. Parackal (2026) Crystal Symmetry and Machine Learning for Systematic Materials Discovery (Doctoral thesis, comprehensive summary) https://dx.doi.org/10.3384/9789181184761

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) https://dx.doi.org/10.1103/PhysRevMaterials.8.113803
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) https://dx.doi.org/10.1103/PhysRevMaterials.8.103801

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.