  Single atomic sites often determine the functionality and performance of
materials, such as catalysts, semi-conductors or enzymes. Computing and
understanding the properties of such sites is therefore a crucial component of
the rational materials design process. Because of complex electronic effects at
the atomic level, atomic site properties are conventionally derived from
computationally expensive first-principle calculations, as this level of theory
is required to achieve relevant accuracy. In this study, we present a widely
applicable machine learning (ML) approach to compute atomic site properties
with high accuracy in real time. The approach works well for complex
non-crystalline atomic structures and therefore opens up the possibility for
high-throughput screenings of nano-materials, amorphous systems and materials
interfaces. Our approach includes a robust featurization scheme to transform
atomic structures into features which can be used by common machine learning
models. Performing a genetic algorithm (GA) based feature selection, we show
how to establish an intuitive physical interpretation of the structure-property
relations implied by the ML models. With this approach, we compute atomic site
stabilities of metal nanoparticles ranging from 3-55 atoms with mean absolute
errors in the range of 0.11-0.14 eV in real time. We also establish the
chemical identity of the site as most important factor in determining atomic
site stabilities, followed by structural features like bond distances and
angles. Both, the featurization and GA feature selection functionality are
published in open-source python modules. With this method, we enable the
efficient rational design of highly specialized real-world nano-catalysts
through data-driven materials screening.
