(Nanowerk Spotlight) The integration of machine learning (ML) into materials science is rapidly transforming the way researchers discover and design advanced ...
Machine learning (ML) models have recently become popular in the field of heterogeneous catalyst design. The inherent complexity of the interactions between catalyst components is very high, leading ...
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Designing the heart of hydrogen cars: AI points to zinc as key for stable fuel cell catalysts
In the era of climate crisis, hydrogen vehicles are emerging as an alternative for eco-friendly mobility. However, the fuel cell, known as the "heart of the hydrogen car," still faces limitations of ...
In the era of climate crisis, hydrogen vehicles are emerging as an alternative for eco-friendly mobility. However, the fuel cell, known as the 'heart ...
The conversion of carbon dioxide into clean fuels is regarded as an important route toward carbon neutrality. CO 2 methanation, in particular, has drawn increasing interest due to its favorable ...
The development of catalysts has long depended on the trial-and-error methods which are time-consuming and often yield inconsistent data. To improve the precision and ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
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