Periodic Reporting for period 1 - MaMMoS (MAgnetic Multiscale MOdelling Suite)
Berichtszeitraum: 2024-01-01 bis 2025-06-30
Context and Motivation
The global transition to green technologies has significantly increased demand for magnetic materials, essential in electric machines such as electric motors and wind turbines. Dependence on rare-earth elements in these magnets creates economic, environmental, and geopolitical challenges, highlighting the urgent need for new magnetic materials with reduced rare-earth content. Traditional development methods for these materials are time consuming and resource heavy, hence MaMMoS has introduced an integrated approach combining experimental characterization, simulations, and artificial intelligence (AI).
Overall Objectives
The Magnetic Multiscale Modelling Suite (MaMMoS) aims to overcome current research limitations by:
- Creating open-access data for magnetic materials from experiments and simulation
- Linking computational modelling with experiments and appropriate AI methods
- Accelerating materials discovery and device design with automated computational workflows and open-source software.
- Strengthening collaboration within EU materials modelling initiatives through a common language (ontology)
Pathway to Impact
MaMMoS supports EU strategic priorities by:
- Reducing reliance on rare-earth elements to enhance European sustainability and independence.
- Accelerating innovation cycles, thereby reducing the time-to-market for green technologies.
- Promoting interoperability through unified ontologies and open-access data repositories.
- Lowering economic and environmental costs through predictive, validated tools.
Scale and Significance of Expected Impacts
MaMMoS has the potential to significantly influence industries such as automotive, renewable energy, and electronics, addressing large market sectors. Environmentally, reducing rare-earth extraction promises substantial ecological benefits. Politically and strategically, MaMMoS advances EU objectives for innovation-driven economic growth and climate neutrality by 2050. Ultimately, the project sets new standards in magnetic materials research, reinforcing Europe's leadership in sustainable technologies and strategic autonomy.
The global transition to green technologies has significantly increased demand for magnetic materials, essential in electric machines such as electric motors and wind turbines. Dependence on rare-earth elements in these magnets creates economic, environmental, and geopolitical challenges, highlighting the urgent need for new magnetic materials with reduced rare-earth content. Traditional development methods for these materials are time consuming and resource heavy, hence MaMMoS has introduced an integrated approach combining experimental characterization, simulations, and artificial intelligence (AI).
Overall Objectives
The Magnetic Multiscale Modelling Suite (MaMMoS) aims to overcome current research limitations by:
- Creating open-access data for magnetic materials from experiments and simulation
- Linking computational modelling with experiments and appropriate AI methods
- Accelerating materials discovery and device design with automated computational workflows and open-source software.
- Strengthening collaboration within EU materials modelling initiatives through a common language (ontology)
Pathway to Impact
MaMMoS supports EU strategic priorities by:
- Reducing reliance on rare-earth elements to enhance European sustainability and independence.
- Accelerating innovation cycles, thereby reducing the time-to-market for green technologies.
- Promoting interoperability through unified ontologies and open-access data repositories.
- Lowering economic and environmental costs through predictive, validated tools.
Scale and Significance of Expected Impacts
MaMMoS has the potential to significantly influence industries such as automotive, renewable energy, and electronics, addressing large market sectors. Environmentally, reducing rare-earth extraction promises substantial ecological benefits. Politically and strategically, MaMMoS advances EU objectives for innovation-driven economic growth and climate neutrality by 2050. Ultimately, the project sets new standards in magnetic materials research, reinforcing Europe's leadership in sustainable technologies and strategic autonomy.
During the reporting period, MaMMoS carried out extensive technical and scientific activities focusing on multiscale modelling, data integration, and advanced characterization of magnetic materials. Key activities included:
- Developing an open-access ontology for magnetic materials, ensuring data interoperability across simulations and experiments.
- Performing substantial computational modelling tasks, including ab-initio simulations, spin dynamics, and micromagnetic modelling, to predict intrinsic and extrinsic magnetic properties.
- Conducting high-throughput experimental characterization of compositionally graded thin films and bulk materials, assessing structural, chemical, and magnetic properties.
- Exploration of appropriate AI models for material science data sets
- Establishing automated computational workflows for optimizing magnetic device geometries and hysteresis properties, significantly accelerating the materials design process.
- Developing an open-source software suite through which the computational workflows can be executed.
Main Achievements
- Creation of a magnetic materials ontology and publication of initial datasets, significantly enhancing interoperability.
- Computational evaluation of rare-earth free and rare-earth reduced materials including Fe2CoH4, doped YFe3 and Fe16N2.Development of AI-driven predictive models that correlate intrinsic and extrinsic micromagnetic properties.
- Creation of MaMMoS software framework and tools.
These achievements substantially advanced the state-of-the-art in magnetic materials research, laying a robust foundation for future innovations and applications.
- Developing an open-access ontology for magnetic materials, ensuring data interoperability across simulations and experiments.
- Performing substantial computational modelling tasks, including ab-initio simulations, spin dynamics, and micromagnetic modelling, to predict intrinsic and extrinsic magnetic properties.
- Conducting high-throughput experimental characterization of compositionally graded thin films and bulk materials, assessing structural, chemical, and magnetic properties.
- Exploration of appropriate AI models for material science data sets
- Establishing automated computational workflows for optimizing magnetic device geometries and hysteresis properties, significantly accelerating the materials design process.
- Developing an open-source software suite through which the computational workflows can be executed.
Main Achievements
- Creation of a magnetic materials ontology and publication of initial datasets, significantly enhancing interoperability.
- Computational evaluation of rare-earth free and rare-earth reduced materials including Fe2CoH4, doped YFe3 and Fe16N2.Development of AI-driven predictive models that correlate intrinsic and extrinsic micromagnetic properties.
- Creation of MaMMoS software framework and tools.
These achievements substantially advanced the state-of-the-art in magnetic materials research, laying a robust foundation for future innovations and applications.
The MaMMoS project has produced significant advancements beyond current capabilities in magnetic materials research.
- New magnetic materials ontology: Developed a comprehensive open-access ontology for magnetic materials, facilitating unprecedented interoperability between simulation and experimental data. This allows broader accessibility and accelerated research collaboration.
- Evaluation of Novel Materials: Machine learning and ab-initio calculation of intrinsic magnetic properties of novel hard magnetic materials (Fe2CoH4) and semi-hard magnetic materials (doped YFe3).
- Developed AI-based prediction of extrinsic properties such as the coercive field for given intrinsic material parameters such as magnetization and anisotropy for a micromagnetic single grain model.
- High-throughput Experimental Methods: Successfully demonstrated advanced techniques for rapid characterization of compositionally graded materials, substantially reducing the time and resources required for material validation in the future.
- Automated Optimization Workflows: Developed automated open source computational workflows for optimizing device geometries and magnetic properties.
- New magnetic materials ontology: Developed a comprehensive open-access ontology for magnetic materials, facilitating unprecedented interoperability between simulation and experimental data. This allows broader accessibility and accelerated research collaboration.
- Evaluation of Novel Materials: Machine learning and ab-initio calculation of intrinsic magnetic properties of novel hard magnetic materials (Fe2CoH4) and semi-hard magnetic materials (doped YFe3).
- Developed AI-based prediction of extrinsic properties such as the coercive field for given intrinsic material parameters such as magnetization and anisotropy for a micromagnetic single grain model.
- High-throughput Experimental Methods: Successfully demonstrated advanced techniques for rapid characterization of compositionally graded materials, substantially reducing the time and resources required for material validation in the future.
- Automated Optimization Workflows: Developed automated open source computational workflows for optimizing device geometries and magnetic properties.