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Register a new userData-driven simulation and characterisation of Au nanoparticles melting
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We develop efficient, accurate, transferable, and interpretable machine learning force fields for Au nanoparticles, based on data gathered from Density Functional Theory calculations. We then use them to investigate the thermodynamic stability of Au nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, with respect to a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with respect to available experimental data. Furthermore, we characterize in detail the solid to liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a rigorous and data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle, and employ it to show that melting initiates at the outer layers.
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Accurate simulations of isotropic permanent magnets require to take the magnetization process into account and consider the anisotropic, nonlinear, and hysteretic material behaviour near the saturation configuration. An efficient method for the solution of the magnetostatic Maxwell equations including the description of isotropic permanent magnets is presented. The algorithm can easily be implemented on top of existing finite element methods and does not require a full characterization of the hysteresis of the magnetic material. Strayfield measurements of an isotropic permanent magnet and simulation results are in good agreement and highlight the importance of a proper description of the isotropic material.
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