About OEDB
The Open Electrolyte Database for Batteries (OEDB) is a comprehensive repository of electrolyte data for battery research and development. Our database contains detailed information about various electrolyte compositions, their properties, and performance characteristics.
This database serves as a valuable resource for researchers, engineers, and students working in the field of battery technology and electrochemistry.
Model Context Protocol (MCP)
The OEDB dataset is delivered through the Model Context Protocol (MCP), for the integration with AI services (e.g., ChatGPT, Claude, Gemini, etc.). By connecting our MCP server to each AI service, researchers can query curated electrolyte records, trigger automated data-processing workflows, retrieve structured exports and so forth.
https://oedb.jp/mcpExamples
How to Connect with ClaudeClick to expand
Connecting to the OEDB MCP Server
Note: Remote MCP connectors are available only to Claude Pro (or higher-tier) accounts.
- 1
Navigate to Connector Settings
Open Claude in your browser and open your profile menu. Choose Settings > Connectors to view the connector list.
- 2
Add the OEDB MCP Server
Scroll to the bottom of the connector list, then click Add custom connector.
In the dialog, enter your preferred Name (e.g, "Open Electrolyte Database for Batteries") and
the following URL in the Remote MCP server URL field:https://oedb.jp/mcp
Confirm to Add to register the OEDB MCP connector.
- 3
Complete Authentication
Click Connect from the connector list. Then, the OEDB MCP server is ready to use from your chat sessions.
How to Connect with ChatGPTClick to expand
Citation
RIS format
Under construction...BibTeX format
Under construction...License
OEDB is released under the CC BY 4.0 (Creative Commons Attribution 4.0 International) license. You are free to share and adapt the data for any purpose, even commercially, provided that appropriate credit or citation is given.
Our Team
Atsuo YAMADA
Director
Professor, Department of Chemical System Engineering, The University of Tokyo
Junichiro SHIOMI
Associate Director
Professor, Department of Mechanical Engineering, The University of Tokyo
Norio TAKENAKA
Technical Lead
Project Lecturer, Department of Chemical System Engineering, The University of Tokyo
Masatoshi HANAI
Project Assistant Professor, Information Technology Center, The University of Tokyo
Shoichi MATSUDA
Team Leader, National Institute for Materials Science
Kou NAKAMURA
PhD Student, Department of Mechanical Engineering, The University of Tokyo
Supports
MEXT Digital Transformation Initiative for Green Energy Materials (DX-GEM/DxMT)
MEXT Advanced Research Infrastructure for Materials and Nanotechnology in Japan (ARIM)
mdx: Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaboration
MEXT Developing a Research Data Ecosystem for the Promotion of Data-Driven Science
