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NICKEFFECT aims to develop novel ferromagnetic Ni-based coating materials to replace the scarce and costly Platinum and ensure high efficiency in key applications.

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Matgenix Tag

30 Sep

“Materials Modelling for Sustainable Materials Development” Webinar with FreeMe project

by Laura Cyrne
in
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Join us for this online event, scheduled for October 22nd at 10:00 CET,  where we will showcase how advanced materials modelling is accelerating the creation of sustainable materials and driving the transition to a greener economy. Hear innovative insights and results directly from NICKEFFECT (represented by consortium partner Matgenix) and the FreeMe project. Discover how sophisticated computational tools are being used to predict, design, and validate new material solutions, from Ni-based alloys to safer coating processes, ensuring innovation meets ecological responsibility. Agenda 10:00 – 10:05 CET (5 min) Welcome & Setting the Scene 10:05 – 10:25 CET (20 min) Presentation 1: NICKEFFECT 10:25 – 10:45 CET (20 min) Presentation 2: FreeMe 10:45 – 10:55 CET (10 min) Joint Panel Q&A Session 10:55 – 11:00 CET (5 min) Closing Remarks Speakers FreeMe Project Kostantinos Pyrgakis, from EXELISIS Dr. Konstantinos Pyrgakis is a Chemical Engineer (NTUA) with an M.Sc. in Computational Fluid Dynamics and a PhD in process systems engineering, specializing in industrial symbiosis and optimal biorefinery design....

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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...

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