Generative AI Engine for Energy Materials Intelligence and Manufacturing

Energy materials development remains experimentally intensive because relevant knowledge is dispersed across heterogeneous sources, material formulas and processing parameters are strongly coupled, and experimental data are often incomplete. Although artificial intelligence (AI), including large language models (LLMs), can support knowledge extraction, materials design, experiment planning and data analysis, these capabilities often operate as isolated tools rather than as a computational–experimental loop. This Perspective proposes a conceptual 4E framework—comprising Extraction, Execution, Evolution, and a Generative AI (GAI) Engine—for energy Materials Intelligence and Manufacturing. Extraction converts literature, experimental records, and multimodal data into structured knowledge. Execution translates encoded recipes into machine-executable protocols for robotic synthesis and characterization while returning real-time feedback. Evolution uses robotic experimental feedback to evaluate a domain-specific Recipe Language Model (RLM), which subsequently recommends new material recipes. The GAI Engine coordinates the RLM, materials database, robotic platform, characterization systems, and digital twin within a closed-loop architecture. The framework is further extended conceptually towards intelligent manufacturing through operational data, process monitoring, quality control, and staged translation from laboratory validation to pilot and industrial operation. The 4E framework therefore provides a pathway, grounded in experimentally demonstrated platforms, that links knowledge, formulas and parameters, robotic execution and feedback-driven learning for Materials Intelligence and Manufacturing.