Machine learning has revolutionized the usage of data, and proven of tremendous applicability due to its ability to find relations in data. One area of application is nanoscience, specifically, the investigation of monolayer protected nanoclusters (MPCs). Experimental research on MPCs requires expensive materials and equipment, and traditional computational research requires costly computation resources. ML is used in this context to alleviate the computational costs by utilizing distance-based regression models as surrogates for expensive Density Functional Theory-based calculations. Our research has so far primarily focused on feature selection, since MPCs provide high-dimensional data. Bio: Joakim Linja received his Ph.D. degree in Mathematical Information Science from the University of Jyväskylä (JYU) in April 2023. He received his Masters degree in physics from JYU in 2017, specializing in nanoscience and computational science. He is currently working as a post doctoral scholar at JYU. His research interests lie in high-performance computing, machine learning, GPU computation, nanoscience and physics.