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GEMPROMISE focuses on optimizing materials science by identifying process parameters that lead to desired material properties. Utilizing high-throughput simulations and machine learning, the project aims to streamline materials discovery, significantly impacting areas like energy transition and sustainable production.
GEMPROMISE aims to tackle the grand challenge of materials science, namely to identify the process parameters leading to a structure with targeted properties and performance.
Compared to the current trial-and-error approach, mastering the Process-Structure-Property-Performance (Proc.→Struc.→Prop.→Perf.) relationships would speed up materials discovery with a huge societal impact (e.g., for energy transition).
From a fundamental standpoint, there is no theory for these relationships.
Thanks to hi…
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
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