Machine Learning-Guided Prediction of Low-Barrier N ₂ and CO ₂ Activation for Catalyst Design

Machine learning (ML) has become an integral component in catalysis research, offering powerful capabilities for predicting and designing low-barrier activation processes for nitrogen (N 2 ) and carbon dioxide (CO 2 ). These transformations are central to realizing sustainable energy conversion and carbon-neutral technologies. In this review, the recent developments in ML-assisted catalysis for N 2 and CO 2 activation are discussed, emphasizing the foundational principles, dataset construction, descriptor engineering, and model interpretability. Applications of supervised learning algorithms, deep neural networks, and active learning frameworks in identifying efficient catalytic systems with reduced energy barriers are examined. Furthermore, the integration of computational predictions with experimental feedback loops is highlighted as a promising approach to accelerate catalyst screening and optimization. Despite notable progress, several limitations remain, including over-fitting in data-scarce regimes, limited transferability to previously unexplored systems, and inadequate confidence estimation in model predictions. More broadly, the widespread adoption of ML in catalysis is hindered by the scarcity of high-quality and diverse datasets, restricted generalizability across different catalytic environments, inherent biases in data generation, and persistent challenges in uncertainty quantification. Overcoming these bottlenecks requires systematic dataset curation, rigorous model validation strategies, and strong interdisciplinary collaboration among chemists, materials scientists, and data engineers. Addressing these issues will enhance the reliability and robustness of ML-driven approaches, thereby accelerating the discovery of efficient catalysts for N 2 fixation and CO 2 reduction. This review also discusses future directions toward the development of intelligent, interpretable, and autonomous platforms for catalyst design.