Datasets for AI‐Driven Energetic Materials Research: Construction, Progress, and Challenges

ABSTRACT Data form the foundational basis for applying artificial intelligence (AI) in energetic materials (EMs), yet dataset construction and curation remain the primary bottlenecks. This work provides a data‐centric overview of the EM data landscape, focusing on construction methodologies, current progress, and challenges. Dataset construction is examined through the lens of data provenance, distinguishing externally sourced data from self‐generated streams. Existing datasets are further categorized according to data modality and application, enabling comparison between public repositories and proprietary or in‐house data assets. To support open research and benchmarking, this review summarizes accessibility information for representative EM datasets. Recent advances in domain‐specific large language models, agentic AI, and autonomous experimental platforms are discussed not merely as automation tools, but as components of emerging data agents capable of supporting adaptive and closed‐loop workflows. Challenges related to data reliability, standardization, accessibility, and governance are further discussed, and a roadmap toward an interoperable and responsibly governed EM data ecosystem is outlined to support reliable data‐driven and AI‐enabled research.∖