Imagine if every scientist working on hydrogen energy had their own separate notebook and never talked to others. This project builds a digital bridge that connects different labs into one giant, smart network. It uses AI to coordinate experiments and data, acting like a central brain that tells researchers exactly what to test next to find the best materials faster.
Green energy material development is too slow because research labs operate in silos. This lack of coordination creates a gap between a lab discovery and a deployable industrial device.
A decentralized cloud platform consisting of the DECODE FABRIC (collaboration matrix), IRL scoring for tool integration, the FOUNDRY knowledge graph, and an AI-enabled CPU for workflow orchestration.
If you are a manufacturer dealing with slow material discovery for electrocatalysts — this project developed a decentralized cloud lab platform that accelerates the path from lab discovery to device deployment. It uses AI-enabled orchestration to reduce R&D timelines.
If you are a producer dealing with fragmented data when synthesizing value-added chemicals — this project developed the DECODE FABRIC and FOUNDRY knowledge graph. These tools link modeling and testing to real-world device metrics.
If you are a software provider dealing with a lack of interoperability between research tools — this project developed an Integration Readiness Level (IRL) scoring system. This allows different characterization and modeling suites to be plugged into a single cloud-connected workflow.
Based on available project data, no pricing or cost structures for the platform have been disclosed.
Yes, the project specifically targets the 'industrialisation of energy materials' and includes industrial partners to bridge the gap between lab-scale tests and real-world conditions.
Based on available project data, specific licensing terms are not mentioned, though the project involves a consortium of 16 partners including 4 industry members.
It uses a scoring concept called Integration Readiness Level (IRL) to assess how well modeling and characterization suites can be integrated into the cloud-connected FABRIC.
The project is active from December 2023 to November 2027, with initial tools and workflows already being developed in the first phase.
The consortium is well-balanced for technology transfer, featuring 16 partners across 8 countries. With a 25% industry ratio (4 companies) and a strong academic/research base (12 partners), the project is structured to move from theoretical modeling to industrial-grade component integration. The presence of an SME and large research centers like Forschungszentrum Jülich suggests a mix of agile development and deep scientific expertise.
Contact Forschungszentrum Jülich GmbH for technical inquiries regarding the DECODE CPU and FABRIC.
Contact us to identify potential integration partners for your energy material R&D pipeline.
