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

Jan Kloppenburg, Livia B. Pártay, Hannes Jónsson, Miguel A. Caro

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
45 Downloads (Pure)

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.

Original languageEnglish
Article number134704
Number of pages9
JournalThe Journal of chemical physics
Volume158
Issue number13
DOIs
Publication statusPublished - 7 Apr 2023
MoE publication typeA1 Journal article-refereed

Fingerprint

Dive into the research topics of 'A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles'. Together they form a unique fingerprint.

Cite this