fig2

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

Figure 2. AI agent-driven workflow for discovering new hydrogen storage materials. (A) The researcher defines design constraints, including material class, elemental composition, and target performance; (B) Using knowledge extracted from > 4,000 publications, the DigHyd agent generates initial candidate compositions; (C) A pretrained machine-learning model then predicts the gravimetric hydrogen capacity of each candidate; (D) The agent iteratively designs, evaluates, and refines candidates within minutes to satisfy user-defined objectives, outputting final compositions together with suggested reaction conditions and a synthetic-feasibility assessment. AI: Artificial intelligence.