A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2023-04-07
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Language
en
Pages
9
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The Journal of chemical physics, Volume 158, issue 13
Abstract
A Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and nanostructured platinum, in particular nanoparticles. Across the range of tested properties, which include bulk elasticity, surface energetics, and nanoparticle stability, this potential shows excellent transferability and agreement with DFT, providing state-of-the-art accuracy at a low computational cost. We showcase the possibilities for modeling of Pt systems enabled by this potential with two examples: the pressure-temperature phase diagram of Pt calculated using nested sampling and a study of the spontaneous crystallization of a large Pt nanoparticle based on classical dynamics simulations over several nanoseconds.
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J.K. and M.A.C. acknowledge funding from the Academy of Finland under the C1 Value Programme, Project No. 329483. M.A.C. also acknowledges personal funding from the Academy of Finland, Project No. 330488. H.J. acknowledges funding from the Icelandic Research Fund, Project No. 207283-053. L.B.P. acknowledges support from the EPSRC through an Early Career Fellowship (Grant No. EP/T000163/1). Computational resources for this project were obtained from the CSC—IT Center for Science and Aalto University’s Science-IT project.
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Citation
Kloppenburg , J , Pártay , L B , Jónsson , H & Caro , M A 2023 , ' A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles ' , The Journal of chemical physics , vol. 158 , no. 13 , 134704 . https://doi.org/10.1063/5.0143891