Research database

GENMAT will develop and validate Europe’s first integrated multi-scale, multi-modal materials-science foundation model (M3FM), shortening the 10–20-year lab-to-market cycle. A unified AI infrastructure will connect atomic chemistry, microstructure evolution, manufacturing optimisation and in-service performance monitoring, providing physics-aware insights from design to use so European industry delivers sustainable materials faster and at lower cost. M3FM combines physics-aware learning with EMMO semantics, multi-modal fusion (chemical structures, process parameters, imaging and sensors), and a hierarchical architecture that bridges scales, under open-science practices for reproducibility and trust. We address three high-risk, high-impact challenges: (1) a FAIR data-governance ecosystem integrating existing resources (e.g., NOMAD) into an interoperable platform; (2) a generalisable backbone that learns across materials classes, replacing fragmented single-task models; and (3) trustworthiness via physics-informed constraints, uncertainty quantification and transparent decision-making.