A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

High‐entropy alloys (HEAs) are highly promising electrocatalysts, but the vastness of their configurational space severely limits traditional screening approaches. To overcome this challenge, this article proposes a conceptual and methodological paradigm shift toward “Inverse Design” (“property‐to‐material”) by introducing the architecture of REACT2COMPO, a neural network framework designed to deduce the optimal composition and atomic distribution of a nanocatalyst directly from targeted macroscopic catalytic properties. To address the lack of experimental 3D structural data required for its training, we introduce a second network, ATOMOD. This model leverages a multi‐fidelity “Sim‐to‐Real” learning strategy: trained exclusively using in silico generated data, it implicitly reconstructs the 3D geometry of the nanoparticle, layer by layer, from a single 2D transmission electron microscopy (TEM) image. Our results demonstrate the feasibility of achieving accurate 3D geometric reconstruction using TEM while highlighting the need for complementary approaches to resolve elements with similar electron scattering cross‐sections. These findings underscore the importance of multimodal data fusion, integrating TEM with complementary techniques such as extended X‐ray absorption fine structure spectroscopy (EXAFS). While the geometric reconstruction from TEM is already operational, this multimodal strategy opens promising perspectives for overcoming the remaining challenges and achieving full 3D chemical resolution. While the geometric reconstruction from TEM is operational, full 3D chemical resolution remains a challenge. This study lays the theoretical and database foundations for a multimodal fusion integrating EXAFS. Once fully implemented, this multi‐scale framework opens a practical pathway to accelerate the discovery of new functional materials.