This master thesis looks at gallium Supported Catalytically Active Liquid Metal (SCALM) systems which show great promise in different catalytic applications, specifically propane dehydrogenation is taken as its main focus. Machine learning interatomic potential models (MLIP) provide a way to accurately simulate large atomic systems at long time scales. This new and improved speed provides the possibility to investigate dynamic systems. A selection of the best currently available models were benchmarked on computational efficiency and accuracy to make an informed choice of MLIP model architecture. Models were finetuned further to improve accuracy with DFT data. Using the best model, MD simulations were performed on gallium nanoparticles of different sizes and solutes. Pt, Pd and Ag were observed to equilibrate in the subsurface as was seen in previous literature. Breaking from this trend, gold was observed to reside in the surface and bismuth was seen to migrate to the surface of the nanoparticle. Clustering MACE descriptors showed it could accurately discern 4 groups: solute atoms, surface atoms, high and low coordination bulk. Furthermore, propane was added to a GaPt SCALM system to capture the dynamic site formation using MD, but none were recorded. The entire computational workflow was designed in Python and can serve as a basis for (multi-architectural) MLIP nanoparticle computational workflows.