176 citations · 1.3k across the 25 of their papers we have counts for
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cond-mat.mtrl-sci2023★ 22 cited
Searching for iron nanoparticles with a general-purpose Gaussian approximation potential
Richard Jana, Miguel A. Caro
We present a general-purpose machine learning Gaussian approximation potential (GAP) for iron that is applicable to all bulk crystal structures found experimentally under diverse t…
cond-mat.mtrl-sci2023★ 22 cited
A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces and nanoparticles
Jan Kloppenburg, Livia B. Pártay, Hannes Jónsson +1
A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on DFT data computed for bulk, surfaces and nanostructured platinum,…