Project description
A closer look at defect-driven phase behaviours in materials
Phase diagrams guide materials innovation, but traditional diagrams assume defect-free materials, whereas real-world microstructures are dominated by defects (grain boundaries, dislocations, and stacking faults) that follow distinct phase behaviours. This limits innovation to slow trial-and-error approaches. The ERC-funded AMASE project aims to introduce multi-defect phase diagrams and microstructure phase maps for accurate defect-aware predictions. It will unify defect descriptions, generate defect-aware free-energy landscapes, and map static and dynamic microstructures. Targeting liquid metal embrittlement in steels, hydrogen embrittlement in Al-alloys, and Mg-alloy formability, AMASE delivers a scalable predictive toolkit for widely used platforms. The goal is to pave the way for faster materials development and transformative industrial solutions.
Objective
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.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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HORIZON.1.1 - European Research Council (ERC)
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(opens in new window) ERC-2025-COG
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12205 Berlin
Germany
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