"DIVE" into Hydrogen Storage Materials Discovery with AI Agents

• URL: http://arxiv.org/abs/2508.13251v2
• Date: Thu, 25 Sep 2025 02:01:31 GMT
• Title: "DIVE" into Hydrogen Storage Materials Discovery with AI Agents
• Authors: 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 summary: Data-driven artificial intelligence (AI) approaches are transforming the discovery of new materials.<n>We present the Descriptive Interpretation of Visual Expression (DIVE) multi-agent workflow, which reads and organizes experimental data from graphical elements in scientific literatures.<n>Building on a curated database of over 30,000 entries from 4,000 publications, we establish a rapid inverse design workflow capable of identifying previously unreported hydrogen storage compositions in two minutes.
• Score: 8.774584882332526
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Data-driven artificial intelligence (AI) approaches are fundamentally transforming the discovery of new materials. Despite the unprecedented availability of materials data in the scientific literature, much of this information remains trapped in unstructured figures and tables, hindering the construction of large language model (LLM)-based AI agent for automated materials design. Here, we present the Descriptive Interpretation of Visual Expression (DIVE) multi-agent workflow, which systematically reads and organizes experimental data from graphical elements in scientific literatures. We focus on solid-state hydrogen storage materials-a class of materials central to future clean-energy technologies and demonstrate that DIVE markedly improves the accuracy and coverage of data extraction compared to the direct extraction by multimodal models, 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 4,000 publications, we establish a rapid inverse design workflow capable of identifying previously unreported hydrogen storage compositions in two minutes. The proposed AI workflow and agent design are broadly transferable across diverse materials, providing a paradigm for AI-driven materials discovery.

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