About this research
Machine-learned interatomic potentials and data-driven workflows accelerating the search for new hydrogen-storage and functional materials.
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2025
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@article{Chandran2025e,
author = {A. Chandran and A. Santhosh and P. Jerabek and R. C. Aydin and C. J. Cyron},
title = {{TiAlNb alloy interatomic potentials: comparing passive and active machine learning techniques with MTP and DeePMD}},
journal = {Front. Mater.},
year = {2025},
volume = {12},
doi = {10.3389/fmats.2025.1591955},
}2024
Comparative analysis of ternary TiAlNb interatomic potentials: moment tensor vs. deep learning approachesopen access
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@article{Chandran2024e,
author = {A. Chandran and A. Santhosh and C. Pistidda and P. Jerabek and R. Aydin and C. J. Cyron},
title = {{Comparative analysis of ternary TiAlNb interatomic potentials: moment tensor vs. deep learning approaches}},
journal = {Front. Mater.},
year = {2024},
volume = {11},
pages = {1466793},
doi = {10.3389/fmats.2024.1466793},
}