Present limitations of large-scale computational approaches are shedding new light on the potential benefit of integrating machine learning (ML) within traditional material science and quantum mechanics . In particular, deep learning (DL) models have shown the ability to effectively capture relevant non-linearities triggered by the atomic configurations of an atomic system . Once trained, the trained DL model can be used for inference in a fraction of the time it would take to run a full DFT calculation, while still producing accurate results. This drastic reduction of time to predict properties of alloys using atomic information opens a promising path towards an effective acceleration of discovery and design of new multi-component alloys. The amount of data required to train DL models for the study of HEAs depends on the dimensionality of the chemical space to explore, the complexity of the DL architecture used, and the availability and quality of the data. In general, DL models benefit from large amounts of diverse and high-fidelity data. However, collecting such large amounts of high-fidelity data is challenging for HEAs. In particular, the design of HEAs often involves exploring a vast compositional space , which means that obtaining a comprehensive dataset covering the entire space is only possible by accessing and leveraging leadership class supercomputing facilities.