Accelerating training of MLIPs through small-cell training

While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable training sets, but these processes can be resource...

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Veröffentlicht in:	Journal of materials research 2023-12, Vol.38 (24), p.5095-5105
Hauptverfasser:	Meziere, Jason A., Luo, Yu, Xia, Yi, Béland, Laurent Karim, Daymond, Mark R., Hart, Gus L. W.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied and Technical Physics Biomaterials Chemistry and Materials Science Density functional theory Inorganic Chemistry Invited Paper Learning Materials Engineering Materials research Materials Science Molecular dynamics Nanotechnology Phase transitions Training Zirconium
Online-Zugang:	Volltext
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