Gold nanoparticles' have unique properties, which make them relevant in catalysis, biomedicine, and optics. These properties dramatically change depending on whether the nanoparticles, and their surface, are in a liquid or solid state. A mechanistic picture of the melting mechanism of small (few nanometers wide) gold particles, as well as a tool to quantitatively predict their melting temperature, is therefore key for their application in cutting-edge technologies. Unfortunately, classical thermodynamics fails in these systems because of the importance of complex surface effects at the nanoscale.
An international study led by SISSA, in collaboration with the École Polytechnique Fédérale de Lausanne, King’s College London, Swansea University and the Aristotle University of Thessaloniki, used machine learning to address the challenge of accurately predicting and characterizing the temperature-dependent phase of small gold nanoparticles. The use of machine learning to predict the forces acting on atoms in the nanoparticles allowed the researchers to quickly conduct long simulations with quantum-mechanical precision. The article has been published in Nature Communications. (Image by iStock)