The KNOSKITE-X project aims to advance the development of next-generation electrode materials for reversible chemical-to-power systems, focusing specifically on solid oxide fuel and electrolysis cells (SOFC/SOEC). These reversible devices operate in two complementary modes: as fuel cells (FC), they convert hydrogen into electricity, and as electrolysis cells (EC), they use excess electricity—particularly from intermittent renewable sources—to produce hydrogen through water electrolysis. This dual functionality enables effective energy storage and contributes to the integration of renewable energy into the electrical grid by converting surplus electricity into a carbon-free fuel.

At the heart of the project is the development of hierarchical porous carbons as well as advanced mixed oxide materials with a perovskite structure. These materials are being designed to minimize reliance on critical raw elements while maintaining high performance and economic feasibility. The overarching goal of KNOSKITE-X is to accelerate material discovery through a knowledge-based approach that integrates state-of-the-art experimental techniques with the power of artificial intelligence (AI).

To achieve this, the project brings together a multidisciplinary methodology that combines tailored material synthesis, cutting-edge characterization techniques, multi-scale modelling, and AI-enabled tools. This approach supports the systematic discovery of scientific knowledge essential for the rational design and optimization of electrode materials.

Reversible SOFC/SOEC technologies are inherently complex, involving interconnected physical and chemical processes such as surface electrochemical reactions, ionic diffusion, charge collection, and conduction—all occurring within highly localized regions. As such, comprehensive understanding requires precise, in-situ characterization under relevant operating conditions, necessitating the use of advanced and non-trivial analytical techniques.

Multi-scale modelling is employed to transform experimental data into predictive scientific insights, reducing the time and cost associated with materials development. The synergy between theoretical and experimental work is ensured through a carefully selected consortium of partners, each with proven expertise in integrating modelling with practical experimentation.

A key innovation of KNOSKITE-X lies in its integration of AI-driven tools, including machine learning and deep learning techniques, to extract meaningful patterns from relatively small datasets. These tools are instrumental in establishing robust composition–structure–activity–performance relationships, paving the way for knowledge-based predictions and streamlined material selection processes.

Beyond scientific discovery, the project is committed to practical outcomes. It aims to develop simplified testing protocols and interoperable tools suitable for industrial use, enabling broader adoption of its findings. To ensure sustainability and accessibility, KNOSKITE-X promotes open science and provides harmonized documentation to facilitate knowledge sharing and long-term impact. In summary, KNOSKITE-X represents a pioneering effort to drive the next wave of energy material innovation through a fusion of experimental excellence, theoretical rigor, and data-driven intelligence.