Computational chemistry is the branch of chemistry that studies chemical systems through simulation and involves HPC architecture and software packages. Python has become an integral part of computational modelling of materials in recent years, with development of packages such as the Atomic Simulation Environment (ASE) which is a set of modules for manipulating, running and visualising atomic simulation. Furthermore, ASE integrates seamlessly with many electronic structure software packages, used for calculating the energy and properties of systems based on some level of theory, e.g Density Functional Theory (DFT). Moreover, the combination with other Python packages that integrate with ASE provide an ecosystem for atomic simulations. Packages such as CatLearn, a machine-learning approach used for calculating energies needed for reactions, along with Phonopy and FHI-vibes, both are for studying lattice dynamics of materials, to name a few, provide a comprehensive toolkit for the computational study of materials and chemical systems

In our research, such approaches are essential to further our understanding of materials and chemical processes, and of particular interest are materials for green and sustainable processes, such as catalysts used to produce fossil fuel alternatives. In this regard, as Python software becomes increasingly popular for the simulation and study of materials, it also provides the tools and methods needed for tackling some of the challenges of today