“DIVE” into hydrogen storage materials discovery with AI agents

Despite the surge of AI in energy materials research, fully autonomous workflows that connect high-precision experimental knowledge to the discovery of credible new energy-related materials remain at an early stage. Here, we develop the Descriptive Interpretation of Visual Expression (DIVE) multi-agent workflow, which systematically reads and organizes experimental data from graphical elements in scientific literature. Applied to solid-state hydrogen storage materials-a class of materials central to future clean-energy technologies-DIVE markedly improves the accuracy and coverage of data extraction compared to the direct extraction method, with gains of 10-15% over commercial models and over 30% relative to…