DIVE-to-design: how a multi-agent workflow converts figure-centric literature into an ai-native hydrogen storage discovery engine

A major bottleneck in artificial intelligence (AI)-driven materials discovery is not model architecture, but limited data accessibility: critical experimental knowledge remains locked in figures, heterogeneous reporting formats, and unstructured PDFs. A recent study by Li et al. addresses this challenge by introducing DIVE (Descriptive Interpretation of Visual Expression), a multi-agent extraction framework that transforms figure-centric scientific content into structured, machine-actionable data. Applied to solid-state hydrogen storage materials, DIVE demonstrates substantial extraction gains over conventional direct large language model (LLM) parsing, then scales to mine 4,053 publications (1972-2025) and build a > 30,000-entry database that powers a downstream inverse-design agent, DigHyd. This work offers a practical blueprint for moving from “LLM-assisted reading” to “AI-enabled discovery infrastructure”, linking literature mining, quality scoring, database construction, and target-driven candidate generation in a single workflow.