DE-centralised Cloud labs fOr inDustrialisation of Energy materials

Digital, Industry & SpaceHORIZON-RIAID: 101135537
EC Contribution
€67,948
Consortium Size
16 orgs
Start Year
2023
β–ΆSummary

DECODE aims at creating and demonstrating a decentralised and adaptable future lab concept that connects multiple labs on a single platform in order to boost the effectiveness and speed-up the development and innovation path for clean energy materials and technologies. Initially demonstrated for selected hydrogen technologies, the DECODE platform is expected to find wide adoption in the clean technology field in the longer run, including energy harvesting, conversion and storage; clean water technologies; and the synthesis of value-added chemicals and fuels. The core of the platform comprises three elements: the DECODE FABRIC that connects adaptative multi-scale modelling and characterisation suites in a matrix-like structure; a scoring concept to assess modelling and characterisation suites in terms of their integration readiness level (IRL); and an AI-enabled central unit (CPU) that processes the IRL scores, performs the technology mapping to the FABRIC and orchestrates contributions in modelling and characterisation from partner labs. For the platform as a whole, DECODE strives to achieve a high level of flexibility, adaptability, and interoperability, in terms of materials modification strategies, technologies and operating regimes that it will be able to handle. Water electrolysis and hydrogen fuel cell technologies are selected for the demonstration of DECODE’s decentralised labs platform. The project will join leading expertise and capabilities in physical theory and modelling, design, fabrication, operando characterisation and testing of functional materials and components, materials digitalisation and cloud-connected lab operations, and industrial-grade component integration and in-line/end-of-line testing and validation by industrial partners in the consortium.

Consortium (16)

Project Results (9)

Source: CORDIS, the EU research results database.

β–ΆPublications (4)
Data-Driven Modeling of Polymer Electrolyte Fuel Cells: Towards Predictive Analytics with Explainable Artificial Intelligence
Energy and AIΒ· 2025DOI
Ali Malek, Max Dreger, Nima Shaigan, Chaojie Song, Kourosh Malek, Jasna Jankovic, Michael Eikerling
Large language models for knowledge graph extraction from tables in materials science
Digital DiscoveryΒ· 2025DOI
Max Dreger, Kourosh Malek, Michael Eikerling
Theory of Electro-Ionic Perturbations at Supported Electrocatalyst Nanoparticles
Physical Review LettersΒ· 2025DOI
Yufan Zhang, Tobias Binninger, Jun Huang, Michael H. Eikerling
UTILE-Pore: Deep Learning-Enabled 3D Analysis of Porous Materials in Polymer Electrolyte Membrane-Based Energy Devices
Journal of The Electrochemical SocietyΒ· 2025DOI
AndrΓ© Colliard-Granero, Salvatore Ranieri, Aimy Bazylak, Tobias Morawietz, K. Andreas Friedrich, Jasna Jankovic, Kourosh Malek, Michael Eikerling, Mohammad Javad Eslamibidgoli
β–ΆDeliverables (5)