  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, 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 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.

Materials Science