SOURCE-LINKED INTELLIGENCE

Assessing Microstructure Phase Maps (AMASE)

CORDIS · observation · Publication date unknown

to deliver “roadmaps”, introducing two novel concepts of Multi-Defect Phase Diagrams and Microstructure Phase Maps for accurate microstructure predictions. AMASE will combine atomistic simulations, machine learning, thermodynamics, and multi-phase-field simulations via a novel CALPHAD-integrated density-based concept. These will be realised through three pillars: first, bridging atomistic simulations, coarse-graining, and machine learning analyses to develop Representative Field Variable(s) that unify descriptions of various defects; second, developing CALPHAD-integrated free energy functionals, iterated with a machine learning framework, and used to generate Multi-Defect Phase Diagrams; and third, spatiotemporal mapping of various microstructures by coupling the results of the first two pillars with a multi-phase-field approach to obtain Static and Dynamic Microstructure Phase Maps. These aims are closely entangled with three critical engineering challenges: (i) mitigating liquid metal embrittlement in steels, (ii) reducing hydrogen embrittlement in Al-alloys, and (iii) improving

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recordType

award

status

SIGNED

region

EU

value

1998000

unit

EUR