Chapter 3: European MI Projects

This chapter provides a comprehensive overview of European Materials Informatics initiatives, highlighting the unique strengths of pan-European collaboration, open science infrastructure, and strategic investments in data-driven materials research across the continent.

3.1 NOMAD (Novel Materials Discovery)

The NOMAD (Novel Materials Discovery) project represents Europe's flagship initiative in computational materials science data infrastructure. Originally launched under Horizon 2020 and continuing through Horizon Europe, NOMAD has built the world's largest repository for computational materials science data.

3.1.1 Project Overview

3.1.2 Key Features and Capabilities

NOMAD provides an integrated ecosystem for computational materials science:

Big-Data Services

• Data Repository: Over 12 million calculations from major DFT codes
• Standardized Formats: Common metadata schema across different simulation codes
• Open Access: Free access to all archived calculations and workflows
• DOI Assignment: Permanent identifiers for reproducibility and citation

AI Workflow Engines

• NOMAD Oasis: Local installation for institutional data management
• AI Toolkit: Pre-built machine learning models for property prediction
• Active Learning: Automated experimental design for high-throughput screening
• Transfer Learning: Pre-trained models for rapid adaptation to new materials systems

Exascale Computing Integration

• EuroHPC Partnership: Integration with European supercomputing resources
• Scalable Workflows: Designed for petascale and exascale computing environments
• Cloud Infrastructure: Elastic computing resources for varying workloads

3.1.3 Organizational Structure

3.1.4 Participating Organizations

Research Groups (8 core partners):

• Fritz Haber Institute, Max Planck Society (Germany) - Coordination
• Humboldt-Universitat zu Berlin (Germany)
• King's College London (UK)
• Barcelona Supercomputing Center (Spain)
• Aalto University (Finland)
• EPFL (Switzerland)
• Technical University of Denmark
• University of Cambridge (UK)

HPC Centers (4 partners):

• SURF (Netherlands)
• Max Planck Computing and Data Facility (Germany)
• CSC - IT Center for Science (Finland)
• CINECA (Italy)

3.1.5 Use Cases and Applications

3.1.6 Data Statistics

3.2 FAIRmat (Germany)

FAIRmat is Germany's flagship initiative for establishing FAIR (Findable, Accessible, Interoperable, Reusable) data infrastructure for condensed-matter physics and materials science, operating as part of the National Research Data Infrastructure (NFDI).

3.2.1 Program Overview

3.2.2 NFDI Context

FAIRmat operates within Germany's broader National Research Data Infrastructure initiative:

• NFDI Mission: Systematic management of scientific data across all disciplines
• Consortium Model: 30+ discipline-specific consortia share common infrastructure
• Funding Structure: Joint federal-state funding (DFG coordination)
• Integration: Cross-consortium data sharing and tool development

3.2.3 Participating Institutions

FAIRmat brings together a broad coalition of German materials science institutions:

3.2.4 International Collaborations

FAIRmat has established formal partnerships with leading international institutions:

3.2.5 Key Infrastructure Components

• NOMAD Integration: FAIRmat extends and enhances NOMAD repository capabilities
• Experimental Data: Standardized schemas for X-ray, neutron, and electron microscopy data
• Electronic Lab Notebooks: Integration with ELN systems for seamless data capture
• Ontologies: Domain-specific vocabularies for materials science
• Training Programs: Workshops and courses on FAIR data practices

3.3 Battery 2030+ / BATT4EU

The Battery 2030+ initiative represents Europe's long-term research strategy for next-generation batteries, while BATT4EU serves as the institutionalized public-private partnership driving European battery innovation.

3.3.1 Initiative Overview

3.3.2 Key Research Projects

BIG-MAP (Battery Interface Genome - Materials Acceleration Platform)

• Budget: 20 million EUR
• Duration: 2020-2024
• Focus: AI-driven discovery of battery materials and interfaces
• Key Output: Autonomous experimentation platform for battery research
• Partners: 34 institutions from 15 countries

INSTABAT (Innovative Sensors for Battery Applications)

• Focus: In-situ sensing technologies for battery health monitoring
• MI Component: Machine learning for state-of-health prediction
• Application: Electric vehicle battery management systems

SENSIBAT (Smart Sensors for Battery Management)

• Focus: Advanced sensing solutions for cell-level monitoring
• Technology: Embedded sensors with edge computing
• Data Integration: Real-time data feeding into digital twin models

SPARTACUS (Spatially Resolved Acoustic, Thermal and Electrochemical Operando Study)

• Focus: Multi-modal operando characterization methods
• Innovation: Combined acoustic, thermal, and electrochemical monitoring
• Data Science: Multi-physics data fusion and analysis

3.3.3 European Battery Funding Timeline

3.3.4 Digital Battery Passport

A key innovation from Battery 2030+ is the Digital Battery Passport:

3.4 Henry Royce Institute (UK)

The Henry Royce Institute is the UK's national institute for advanced materials research and innovation, named after the pioneering engineer Sir Henry Royce. It represents the UK's largest investment in materials research infrastructure.

3.4.1 Institute Overview

3.4.2 Partner Network

The Henry Royce Institute operates as a distributed network across nine UK universities:

• University of Manchester - National Hub and Coordination
• University of Cambridge - Semiconductors, Energy Materials
• University of Oxford - Functional Materials, Devices
• Imperial College London - Structural Materials, Manufacturing
• University of Leeds - Polymers, Soft Materials
• University of Liverpool - Materials Chemistry
• University of Sheffield - Nuclear Materials, Metallurgy
• National Nuclear Laboratory - Nuclear Materials
• UKAEA - Fusion Materials

3.4.3 Research Themes

The Royce Institute organizes research around key societal challenges:

Low Carbon Power

• Battery materials and manufacturing
• Hydrogen storage and fuel cells
• Photovoltaic materials
• Nuclear materials (fission and fusion)

Circular Economy

• Materials recycling and recovery
• Sustainable materials design
• Lifecycle assessment integration
• Bio-based and biodegradable materials

Digital Materials

• Materials Informatics platforms
• Digital twins for materials processing
• AI-guided materials discovery
• Automated characterization and analysis

3.4.4 Materials 4.0 Centre for Doctoral Training

A flagship educational program within the Royce ecosystem:

3.4.5 Equipment and Capabilities

The Royce Institute provides world-class research infrastructure:

• Advanced Electron Microscopy: Aberration-corrected TEM/STEM facilities
• Atom Probe Tomography: 3D atomic-scale characterization
• Synchrotron Access: Diamond Light Source partnership
• Processing Equipment: Pilot-scale manufacturing facilities
• Computing Resources: HPC clusters for simulation and MI

3.5 Other European Initiatives

Beyond the major programs described above, several other European initiatives contribute significantly to the MI landscape.

3.5.1 Empa (Switzerland)

Empa - Swiss Federal Laboratories for Materials Science and Technology operates as Switzerland's premier materials research institution:

Key MI Programs at Empa:

• Materials Data Science and Informatics group
• Self-driving laboratories for materials synthesis
• Digital twin development for manufacturing processes
• Integration with Swiss Data Science Center (SDSC)

3.5.2 Paul Scherrer Institute (PSI, Switzerland)

PSI is Switzerland's largest research institute, hosting unique large-scale research facilities:

• Swiss Light Source (SLS): Synchrotron for materials characterization
• SINQ: Continuous neutron source for structural studies
• SwissFEL: X-ray free-electron laser for ultrafast dynamics
• Data Science Focus: Automated analysis of large-scale facility data

3.5.3 EOSC-Pillar (European Open Science Cloud)

The European Open Science Cloud provides overarching data infrastructure supporting materials science:

• Mission: Federated research data infrastructure for Europe
• Materials Connection: Integration of NOMAD, FAIRmat with broader EOSC ecosystem
• Services: Data storage, computing resources, analysis platforms
• Standards: FAIR principles implementation across disciplines

3.5.4 Max Planck Institutes (Germany)

Several Max Planck Institutes contribute significantly to European MI:

• Fritz Haber Institute: NOMAD coordination, computational catalysis
• MPI for Iron Research: Computational metallurgy, alloy design
• MPI for Polymer Research: Soft matter informatics
• MPI for Solid State Research: Electronic structure calculations

3.6 Comparison of European MI Projects

The following table summarizes key characteristics of major European Materials Informatics initiatives:

3.7 European Funding Landscape

Understanding the European funding ecosystem is essential for engaging with EU MI initiatives:

3.7.1 Horizon Europe Framework

3.7.2 NOMAD and FAIRmat Funded Projects (2015-2024)

The NOMAD Laboratory and FAIRmat consortium have received significant funding from European and German sources:

3.7.3 Horizon 2020 / Horizon Europe Materials Projects (2015-2024)

The European Union's framework programs have funded numerous large-scale materials informatics initiatives:

3.7.4 UK EPSRC Materials Informatics Funding (2015-2024)

The UK Engineering and Physical Sciences Research Council has made substantial investments in materials informatics:

3.7.5 National Funding Streams

• Germany: DFG (German Research Foundation), BMBF (Federal Ministry of Education and Research)
• UK: UKRI (UK Research and Innovation), EPSRC (Engineering and Physical Sciences Research Council)
• France: ANR (French National Research Agency), CNRS
• Switzerland: SNF (Swiss National Science Foundation), Innosuisse

3.7.6 Funding Timeline Overview

3.8 Timeline of European MI Development

3.9 European MI Ecosystem Structure

3.10 Chapter Summary

Europe has developed a distinctive approach to Materials Informatics characterized by large-scale collaboration, open science principles, and strong integration between academic and industrial partners.

Key Takeaways

• NOMAD (2015-present): World's largest computational materials science repository with 12+ million calculations; exemplifies European commitment to open data
• FAIRmat (2019-2028): Germany's 10-year investment in FAIR data infrastructure connects 60 PIs from 34 institutions with international MoUs
• Battery 2030+ / BATT4EU: 925 million EUR public-private partnership driving next-generation battery innovation with Digital Battery Passport
• Henry Royce Institute: UK's 250+ million GBP investment in advanced materials infrastructure across 9 partner universities
• Swiss Excellence: Empa and PSI contribute world-class facilities and digital lab capabilities

Practical Recommendations

• For Researchers: Deposit data in NOMAD, apply for Horizon Europe funding, engage with EOSC infrastructure
• For Industry: Join BATT4EU or sector-specific partnerships, access Royce facilities for collaborative research
• For Students: Consider Materials 4.0 CDT or similar programs, build skills in both computation and experiment
• For International Collaborators: Establish MoUs with FAIRmat, contribute to NOMAD, participate in Horizon Europe calls

Exercises

References

1. Draxl, C., Scheffler, M. (2019). The NOMAD laboratory: from data sharing to artificial intelligence. Journal of Physics: Materials, 2(3), 036001.
2. Scheffler, M., et al. (2022). FAIR data enabling new horizons for materials research. Nature, 604, 635-642.
3. Battery 2030+ Roadmap. (2020). Inventing the sustainable batteries of the future. European Commission.
4. FAIRmat Consortium. (2024). FAIR Data Infrastructure for Condensed-Matter Physics - Annual Report 2023.
5. Henry Royce Institute. (2023). Strategic Plan 2023-2028: Advanced Materials for Clean Growth.
6. BATT4EU. (2024). Batteries European Partnership - Strategic Research and Innovation Agenda.
7. Himanen, L., et al. (2019). Data-driven materials science: status, challenges, and perspectives. Advanced Science, 6(21), 1900808.