“DIVE” into hydrogen storage materials discovery with AI agents
Di Zhang, Xue Jia, Tran Ba Hung, Seong Hoon Jang, Linda Zhang, Ryuhei Sato, Yusuke Hashimoto, Toyoto Sato, Kiyoe Konno, Shin‐ichi Orimo, Hao Li
Abstract
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 open-source models. Building on a curated database of over 30 000 entries from >4000 publications, we establish a rapid inverse-design AI workflow capable of proposing new materials within minutes. This transferable, end-to-end paradigm illustrates how multimodal AI agents can convert literature-embedded scientific knowledge into actionable innovation, offering a scalable pathway for accelerated discovery across chemistry and materials science.