Vyhodnocení přesnosti potenciálů neuronových sítí pro simulace platinových nanoklastrů na hydroxylovaných křemičitých površích

Pokorná, Kristýna

Supported platinum nanoparticles are important heterogeneous catalysts in many industrial processes, but their activity is strongly affected by particle diffusion and sintering mechanisms which lead to deactivation of the catalyst. To stabilise Pt nanoparticles, it is necessary to understand the reactive interactions of Pt with its support material, e.g., hydroxylated silica and defect-containing zeolites. Realistic simulations of such catalysts at the relevant timescales can be achieved with Neural Network Potentials (NNP) which retain ab initio accuracy at about 103 times lower computational costs compared to density functional theory (DFT) calculations. However, NNPs have only limited transferability to systems not included in the training database. Therefore, in this work recently developed SchNet NNPs were thoroughly tested. These NNPs were trained on a diverse set of Pt and defect-containing zeolites and hydroxylated silica surfaces. Firstly, the DFT database was extended by an active learning approach to accurately model the surfaces of α-quartz, MWW as well as the 2D zeolite layer IPC-1P (hydrolysis product of UTL). The NNPs trained on the new DFT database were then tested using MD simulations of systems unseen during the training procedure. These systems include a silanol nest containing...

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