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Generative machine learning for combined process control and materials design

CORDIS · observation · Publication date unknown

Generative machine learning for combined process control and materials design 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 highthroughput (HT) ab initio simulations, Struc.→Prop. (and hence Prop.→Perf.) can be well predicted and machine-learning (ML) approaches have been recently used as much faster surrogate models. But the simulation of the complete Proc.→Struc. is still out of reach, and ML approaches are hindered by the lack of data given the vast am

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recordType

award

status

SIGNED

region

EU

value

9314934

unit

EUR