Welcome to
NICKEFFECT

Boosting the research and development of new solutions 
for materials replacing the Platinum Group Metals (PGM)

NICKEFFECT in a Nutshell

NICKEFFECT, a new project co-funded by the European Commission’s Horizon Europe programme, aims to develop novel ferromagnetic Ni-based coating materials to replace the scarce and costly Platinum and ensure high efficiency in key applications.  

Running from June 2022 until June 2026, the NICKEFFECT project is led by a consortium that is a multidisciplinary team comprised of 12 partners from 7 different EU and HEU-associated countries (Belgium, France, Germany, Greece, Ireland, Spain, and the United Kingdom). It covers stakeholders of the whole project value chain: scientific and technology developers, technology providers, end-users, as well as transversal partners.

Project Goals and Objectives

Synthesise ferromagnetic coating materials to replace Platinum as raw material;

Develop measures to ensure that the materials are affordable, durable and with increased corrosion resistance for the different working environments;

Successfully upscale production process in pilot plant to coat real scale components;

Ensure a safe and sustainable by-design approach and define pathways for the recovery, recyclability, purification and re-use of materials at the end of the products life;

Develop a decision support tool to facilitate the adoption of the safe and sustainable criteria when designing and producing metallic coatings free of PGMs;

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Latest News

Setting the Standard for Green Hydrogen: The Journey of NICKEFFECT’s CEN Workshop Agreement

The NICKEFFECT project has successfully bridged the gap between laboratory research and industrial application with the publication of CWA 18302:2026, a landmark CEN Workshop Agreement developed in collaboration with CEN-CENELEC. This document establishes the first harmonized European protocol for the electrochemical characterization of non-noble, porous metal-based electrodes for hydrogen generation in acidic media. By standardizing testing cells, activity parameters, and data reporting, this success story—led by CIDETEC, the Spanish Association for Standardization (UNE), and project coordinator Aliona Nicolenco—provides the scientific community with a critical tool to accelerate the development of cost-effective, PGM-free water electrolysis technologies, ensuring that early-stage material innovations can be reliably benchmarked and scaled for a net-zero future.

Generative Artificial Intelligence for Direct Compound-Properties-Synthesis Optimization

The NICKEFFECT project leverages cutting-edge computational strategies to revolutionize the design of magnetic Ni-Co alloys for RAM applications. While traditional first-principles methods like Density Functional Theory (DFT) and modern universal machine-learning interatomic potentials have significantly accelerated the characterization of known structures, the search for entirely new materials has historically been limited by the vast dimensionality of chemical space. To overcome this, we are integrating Generative Artificial Intelligence (GenAI), which encodes complex crystal structures into lower-dimension latent spaces to suggest novel compounds. Moving beyond a simple "generate-then-screen" workflow, our latest research—developed in collaboration with Matgenix—explores co-modality alignment. By synchronizing latent spaces for specific physical properties and synthesis parameters, we can directly optimize the generation of compounds that meet precise performance targets and manufacturing requirements, setting a new standard for efficient, objective-driven materials discovery.

Turning Machine Learning Experiments into Impact: Why MLOps in Materials Innovation

In the pursuit of materials innovation, an accurate machine learning model is only the beginning. Within the NICKEFFECT project, we are bridging the gap between isolated experiments and scalable scientific impact by integrating robust MLOps (Machine Learning Operations). By utilizing tools like MLflow to ensure every dataset, parameter, and model version is traceable and reproducible, we’ve transformed complex data into a dependable digital decision-support system. Discover how our structured MLOps workflow—from automated tracking to REST API integration—is setting a new standard for transparency and efficiency in materials science research.

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