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08 Sep

Data Management for Safe and Sustainable Innovation in NICKEFFECT

Designing next-generation materials that are not only high-performing but also safe and sustainable is an inherently complex task. It requires navigating a landscape marked by interdisciplinary data, multiple stakeholder needs, regulatory constraints, and the urgency to make early-stage design decisions with lasting consequences. In the NICKEFFECT project, these challenges are being met with a powerful data management and decision support infrastructure designed to foster collaboration, improve traceability, and accelerate safe and sustainable innovation. At the heart of this infrastructure lies a dedicated Materials and Processes Information Management System (M&P IMS), deployed using the Ansys Granta MI platform [1], and a modular Decision Support System (DSS). These tools provide secure, structured access to critical information—ranging from experimental performance data to risk assessments and environmental impacts—enabling researchers, sustainability experts, and regulators to make informed choices throughout the R&D process. [caption id="attachment_7429" align="aligncenter" width="576"] Figure 1. The NICKEFFECT M&P IMS interface accessible via browser with...

28 Aug

Smarter, Faster, More Transparent: Active Learning and AI in Materials Design

by Laura Cyrne

in Articles, News

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Materials design remains a very complex challenge in science and engineering, owing to the virtually infinite number of variables such as material composition and synthesis parameters. Traditionally guided by expert intuition and iterative experimentation, the process often involves navigating a multidimensional design space with only fragmentary knowledge, which can be compared to exploring a vast territory with a map that covers only a few percent of the terrain. As a result, identifying optimal material candidates frequently requires extensive trial-and-error, consuming significant time and resources before reaching a viable solution. To accelerate this process, materials scientists are increasingly turning to models based on data to guide their research by prioritizing the next materials to study or synthesis parameters to use. In the last decade, machine learning (ML) has gained popularity in materials design thanks to its efficiency and modularity in using a wide range of datasets, whether theoretical or experimental, large or...

28 Jul

Cracking the Code of Clean Hydrogen

by Laura Cyrne

in Articles, Events, News

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How a common metal like Nickel could replace Platinum in electrolysers and accelerate the EU's green transition Imagine trying to replace the world’s best performer with a budget alternative—and not just match it, but get close enough to change the game. That’s the challenge facing scientists working to reduce the use of platinum in hydrogen production. And it’s the bold mission of the Horizon Europe project NICKEFFECT. Why Hydrogen, and why now? Hydrogen is a key pillar of the European Union’s plan to decarbonise its economy and reach climate neutrality by 2050. Produced through water electrolysis, hydrogen offers a clean energy carrier—especially when powered by renewables. The EU has set an ambitious target: installing 40 GW of electrolyser capacity by 2030, which would produce millions of tonnes of green hydrogen annually. But there's a catch. The most efficient technology for hydrogen production—Proton Exchange Membrane Water Electrolysers (PEMWE)—relies on rare and expensive platinum group metals...

Data Management for Safe and Sustainable Innovation in NICKEFFECT

Smarter, Faster, More Transparent: Active Learning and AI in Materials Design

Cracking the Code of Clean Hydrogen

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