A multimodal semantic learning framework for data-driven discovery of nitrate-to-ammonia electrocatalysts

Abstract

Data-centric approaches are increasingly driving the discovery of functional materials; however, the heterogeneity of experimental conditions and the fragmented nature of data, especially when embedded in unstructured scientific literature, hinder the development of generalizable predictive models. Here, we present a multimodal artificial intelligence (AI) framework, the Material Semantics Network (MatSemNet), which learns materials semantics by integrating textual, numerical, and reaction pathway information extracted from the literature. By constructing a unified representation of structure-property relationships, MatSemNet enables transferable prediction of catalytic performance across material systems, outperforming conventional single-modal or descriptor-based approaches. As a proof of concept, we apply MatSemNet to the nitrate reduction reaction (NO3RR), achieving high predictive accuracy for Faradaic efficiency (FE) and identifying promising catalyst candidates. Guided by the model, we design and experimentally validate rare-earth (RE)-doped Co3O4 catalysts, which deliver an FE of 85.5% and an ammonia yield rate of 28.3 mg h−1 cm−2 under alkaline conditions. Mechanistic studies show that RE incorporation modulates the electronic structure and enhances reaction selectivity. Beyond this specific application, MatSemNet offers a scalable and transferable paradigm for data-driven materials discovery by bridging heterogeneous knowledge with predictive modeling, and it is broadly applicable to diverse catalytic and functional material systems.