logo

Assessing Microstructure Phase Maps (AMASE)

Sector: Hydrogen • Location: Germany

Source: EU Funding & Tenders Portal

Project
Forthcoming

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

Project Information FAQ

Project Information

4 Q
The project “Assessing Microstructure Phase Maps (AMASE)” is an infrastructure initiative in the Hydrogen sector, located in Germany. Taiyo aggregates data on it from EU Funding & Tenders Portal.

Want to explore the full details? View the full report

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

Contact name

Obfuscated Data

Phone

0000000000

Email

ObfuscatedData@email.com

Address

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

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

Country

Obfuscated

State

Obfuscated Data

County

Obfuscated

Location

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Source

Source reliability

High

Data quality score

100%

Source

Obfuscated Data

URL

obfuscated_data,obfuscateddata.com

More Details

Project Type

Obfuscated Data

Article Published Date

Obfuscated Data