This task focuses on the identification and preliminary selection of key physical, thermal, and mechanical properties critical for space applications. The evaluation will include properties such as thermal conductivity, carrier mobility, electrical conductivity, absorption/emission spectrum, chemical reactivity, surface wettability, and adhesion. The objective is to establish a comprehensive dataset of material attributes that align with the stringent environmental and operational demands of space missions.

This task involves the classification and organization of existing computational methodologies that support the predictive modelling of material properties. Beginning with traditional simulation techniques such as molecular dynamics, Monte Carlo methods, and multiscale physics approaches, an arborescence (hierarchical structure) of available computational tools and software will be developed. Special emphasis will be placed on machine learning and generative predictive models, evaluating their efficacy and reliability compared to conventional simulation frameworks. The goal is to determine the most effective computational strategies for designing and optimizing materials tailored for space applications.