Assessing Microstructure Phase Maps (AMASE)
Sector: Hydrogen • Location: Germany
Source: EU Funding & Tenders Portal
Phase diagrams are “treasure maps” in materials innovation. However, they traditionally assume defect-free materials, whereas real-world microstructures are often dominated by defects––such as grain boundaries, phase boundaries, dislocations, and stacking faults––that have their own phase behaviours and distinct rules for evolving, interacting, and co-existing. This discrepancy significantly ties
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Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | forthcoming |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
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Description
Description | Phase diagrams are “treasure maps” in materials innovation. However, they traditionally assume defect-free materials, whereas real-world microstructures are often dominated by defects––such as grain boundaries, phase boundaries, dislocations, and stacking faults––that have their own phase behaviours and distinct rules for evolving, interacting, and co-existing. This discrepancy significantly ties materials innovation to slow, trial-and-error approaches. Project AMASE is envisioned 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 the formability of Mg-alloys. Built on the PI’s pioneering contributions in defect thermodynamics and scale-bridging methods, AMASE will deliver a scalable predictive toolkit compatible with widely used platforms such as Thermo-Calc, pyCALPHAD, and OpenPhase, promising to significantly improve development cycles and setting a new paradigm that offers transformative solutions for high-impact industrial challenges. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
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Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
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