Data Ecosystems for Machine-Learning Assisted Materials Design: Insights From OXD-Like Nanozyme and ORR Catalysts

Oxidase-like (OXD-like) nanozymes are vital biomimetic catalysts that regulate oxygen activation and reactive oxygen species generation, whereas oxygen reduction reaction (ORR) catalysts are key cathodic catalysts in energy conversion devices. Although both systems share the O₂-to-H₂O₂/H₂O pathways, machine-learning (ML) studies of them have diverged, with ORR limited by idealized data ecosystems and OXD-like catalysis hindered by heterogeneous datasets. Accordingly, with ORR and OXD-like catalysts as representative examples, this review centers on data-driven ML-assisted materials design and highlights the differentiated data ecosystems within chemical science. We provide a detailed discussion of database construction, feature selection, and ML workflows. Furthermore, we show that data origin critically shapes ML studies, with theory-derived ORR databases often being overly idealized and literature-derived OXD databases highly noisy, thereby affecting structural representations, descriptor dimensionality, high-throughput feasibility, and research paradigms. Overall, this review provides methodology- and perspective-oriented guidance for chemistry and materials science from the perspective of ORR and OXD-like catalysts. We aim to outline a data-centered ML-assisted materials design logic that could inform broader materials science research. This logic integrates automated high-throughput experimentation, iterative active learning, and high-throughput characterization, and provides a progressive framework linking experimental protocol optimization, high-performance material discovery, and the extraction of intrinsic principles.