EMMC 2027 – 6th EMMC International Workshop
EMMC 2027, part of the biennial EMMC International Workshop series, will take place in Leuven, Belgium, from 20–22 April 2027.
The EMMC 2027 International Workshop brings together researchers, industrial innovators, software developers, data scientists, and policy experts to explore the theme “Materials Research in Transition.”
At a time when materials science is being reshaped by rapid advances in AI on data-driven workflows, modelling technologies, software development and industrial innovation, and digital infrastructures, the workshop provides a unique forum to discuss how Europe can build a strong, impactful and sustainable digital infrastructure for advanced materials.
The workshop will examine the profound transformation of materials research, from physics-based simulation to AI-enabled modelling, from fragmented datasets to connected materials knowledge, and from conventional workflows to increasingly autonomous discovery systems. Participants will explore practical, reliable, and sustainable pathways for industrial adoption of AI and modelling, while addressing the skills, software, and community solutions needed to thrive in a rapidly evolving research landscape.
Discussions will also focus on accelerating materials innovation from design to application, new materials innovation models for industry, and establishing robust governance frameworks that ensure the long-term sustainability of European digital research infrastructures.
Together, these conversations will help shape the next generation of digital materials research and innovation in Europe.
The 2027 theme, “Materials Research in Transition”, will be examined across six half‑day sessions, each addressing a major transformation shaping the field:
- Modelling – from physics‑based simulation to AI‑enabled materials models
- Workflows – from connected research workflows to autonomous discovery
- Data – from fragmented datasets to connected materials knowledge
- Software – building and sustaining materials science software in the age of AI
- Industrial Innovation Ecosystems – transformations in the industrial materials R&D ecosystem
- Policy & Governance – frameworks for sustainable digital materials research infrastructures
The workshop will offer keynotes, expert panels, interactive discussions, poster sessions and a hackathon.
The EMMC International Workshopis a leading, cross-cutting event where stakeholders from different materials & digital fields in industry and academia get together to discuss topics of strategic importance and elaborate on gaps and potential
Navigation
Programm
Registration & Welcome Coffee
Welcome by EMMC
SESSION 1
Modelling in transition: From Physics-Based Simulation to AI-Enabled Materials Models
Modelling in transition: From Physics-Based Simulation to AI-Enabled Materials Models
This session will explore how materials modelling is evolving through the integration of established physics-based approaches with machine learning, generative AI and emerging foundation models. Rather than replacing physical understanding, these methods can complement first-principles calculations, atomistic simulations and continuum modelling by accelerating predictions, linking different length and time scales, and identifying patterns in increasingly complex datasets.
The session aims to examine how hybrid physics–AI models can improve accuracy, efficiency, interpretability and generalisation, while reducing the amount of training data required. It will also address the potential of foundation models trained on diverse materials data to support multiple tasks, including property prediction, structure generation, simulation, inverse design and experimental planning.
Topics in scope of the session
- Physics-informed and physics-constrained machine learning
- Integration of AI with first-principles, atomistic and continuum models
- Materials foundation models and large scientific models
- Multiscale and multiphysics modelling
- Generative models and inverse materials design
- Uncertainty quantification, explainability and model validation
- Transferability across materials classes, scales and applications
- Data quality, benchmarking and reproducibility
- Computational infrastructure and access to advanced models
- The evolving role of theory, domain knowledge and human expertise
Session 1 – Description
Chair
Bio
Kersti Hermansson is Professor of Inorganic Chemistry at Uppsala University. Her group conducts research on multi-scale modelling workflows to enable (adequately) accurate simulations for real-world chemical applications. The group has much experience with redox chemistry of nano-structured metal oxides, electrochemistry and fundamentals of water/solid interfaces, molecular diffusion and interactions in solids, and computational vibrational spectroscopies.
She is a Fellow of the Royal Swedish Academy of Sciences (KVA) and member of the Board of Directors of the EMMC ASBL.
Kersti Hermansson
Uppsala University, Sweden
Bio
Dr Maria Alfredsson obtained her PhD in 1999 from Uppsala University, studying H-bonded systems using first principles simulations.
After graduation she received a Research Fellowship from the Swedish Research Council to study metal/oxide interfaces at the Royal Institution of Great Britain in London, UK, followed by a Post-Doctoral research position at the University of College, London, in the field of environmental sciences.
Since, 2007 she holds a permanent position at the University of Kent.
Her research is focused on materials for energy and environment, including experimental work, e.g. synchrotron spectroscopy techniques as well as theoretical studies, for interpretation and prediction of experiments.
Maria Alfredsson
University of Kent, United Kingdom
Plenary 1
Coffee break
SESSION 1
Presentations & Discussion
Modelling in transition: From Physics-Based Simulation to AI-Enabled Materials Models
This session will explore how materials modelling is evolving through the integration of established physics-based approaches with machine learning, generative AI and emerging foundation models. Rather than replacing physical understanding, these methods can complement first-principles calculations, atomistic simulations and continuum modelling by accelerating predictions, linking different length and time scales, and identifying patterns in increasingly complex datasets.
The session aims to examine how hybrid physics–AI models can improve accuracy, efficiency, interpretability and generalisation, while reducing the amount of training data required. It will also address the potential of foundation models trained on diverse materials data to support multiple tasks, including property prediction, structure generation, simulation, inverse design and experimental planning.
Topics in scope of the session
- Physics-informed and physics-constrained machine learning
- Integration of AI with first-principles, atomistic and continuum models
- Materials foundation models and large scientific models
- Multiscale and multiphysics modelling
- Generative models and inverse materials design
- Uncertainty quantification, explainability and model validation
- Transferability across materials classes, scales and applications
- Data quality, benchmarking and reproducibility
- Computational infrastructure and access to advanced models
- The evolving role of theory, domain knowledge and human expertise
Session 1 – Description
Lunch
SESSION 2
Workflows in transition: from sequential research workflows to autonomous discovery
Workflows in transition: from sequential research workflows to autonomous discovery
This session will examine how materials-research workflows are evolving from largely sequential and discipline-specific processes into integrated, data-rich and increasingly autonomous systems. The focus will be on connecting modelling, synthesis, experimentation and characterisation within closed-loop workflows that can continuously learn, adapt and optimise.
Particular attention is given to the development of self-driving laboratories, where automated experimentation, real-time characterisation, machine learning and decision-making algorithms work together to accelerate materials discovery and process optimisation. The session also aims to address the practical challenges of integrating instruments, software, data standards and human expertise across different research environments.
Topics in scope of the session
- Integration of modelling, synthesis and characterisation
- Closed-loop and adaptive experimentation
- Automated and high-throughput laboratories
- Self-driving laboratories and autonomous decision-making
- Real-time and in situ characterisation
- Digital twins for experiments and processes
- Workflow orchestration and laboratory interoperability
- FAIR data, metadata and common data standards
- Robotics and intelligent instrumentation
- Reproducibility, validation and human oversight
- Scaling autonomous workflows from individual laboratories to shared facilities
Session 2 – Description
Chair
Bio
Costas Charitidis is Professor in the School of Chemical Engineering of the National Technical University of Athens and Director of the Laboratory of Advanced, Composite, Nano Materials & Nanotechnology.
He is member of the Scientific Council of the Hellenic Foundation for Research and Innovation. He has been elected in the Deanship of the School of Chemical Engineering of NTUA since 2017.
He is one of the founding (in 2014) and organizational members of EMCC.
Costas Charitidis
NTUA/RNanoLab, Greece
Plenary 2
SESSION 2
Presentations & Discussions
Workflows in transition: from sequential research workflows to autonomous discovery
This session will examine how materials-research workflows are evolving from largely sequential and discipline-specific processes into integrated, data-rich and increasingly autonomous systems. The focus will be on connecting modelling, synthesis, experimentation and characterisation within closed-loop workflows that can continuously learn, adapt and optimise.
Particular attention is given to the development of self-driving laboratories, where automated experimentation, real-time characterisation, machine learning and decision-making algorithms work together to accelerate materials discovery and process optimisation. The session also aims to address the practical challenges of integrating instruments, software, data standards and human expertise across different research environments.
Topics in scope of the session
- Integration of modelling, synthesis and characterisation
- Closed-loop and adaptive experimentation
- Automated and high-throughput laboratories
- Self-driving laboratories and autonomous decision-making
- Real-time and in situ characterisation
- Digital twins for experiments and processes
- Workflow orchestration and laboratory interoperability
- FAIR data, metadata and common data standards
- Robotics and intelligent instrumentation
- Reproducibility, validation and human oversight
- Scaling autonomous workflows from individual laboratories to shared facilities
Session 2 – Description
Poster presentations
Poster Session & Networking Event
Welcome by EMMC
SESSION 3
Data in transition: From Fragmented Datasets to Connected Materials Knowledge
Data in transition: From Fragmented Datasets to Connected Materials Knowledge
This session will explore the transition from fragmented, application-specific datasets towards connected and machine-actionable materials knowledge. It will focus on how linked data, ontologies, common vocabularies and semantic technologies can enable information to move more reliably across modelling, characterisation, synthesis and manufacturing workflows.
The session aims to examine how semantic interoperability allows data produced by different instruments, laboratories and computational tools to be interpreted and reused without losing scientific context. It will also address the role of FAIR data principles, persistent identifiers, provenance and knowledge graphs in supporting reproducibility, AI-ready datasets and collaboration across organisations and disciplines.
Topics in scope of the session
- Linked data and materials knowledge graphs
- Ontologies, taxonomies and controlled vocabularies
- Semantic interoperability across disciplines and platforms
- FAIR and machine-actionable data
- Metadata, provenance and persistent identifiers
- Connecting experimental and computational data
- Interoperability between instruments, repositories and research infrastructures
- Semantic annotation and automated data integration
- Data quality, validation and trust
- Standards and community governance
- Preparing materials data for AI and foundation models
- Intellectual property, access and data-sharing models
Session 3 – Description
Chair
Plenary 3
SESSION 3
Presentations & Discussion
Data in transition: From Fragmented Datasets to Connected Materials Knowledge
This session will explore the transition from fragmented, application-specific datasets towards connected and machine-actionable materials knowledge. It will focus on how linked data, ontologies, common vocabularies and semantic technologies can enable information to move more reliably across modelling, characterisation, synthesis and manufacturing workflows.
The session aims to examine how semantic interoperability allows data produced by different instruments, laboratories and computational tools to be interpreted and reused without losing scientific context. It will also address the role of FAIR data principles, persistent identifiers, provenance and knowledge graphs in supporting reproducibility, AI-ready datasets and collaboration across organisations and disciplines.
Topics in scope of the session
- Linked data and materials knowledge graphs
- Ontologies, taxonomies and controlled vocabularies
- Semantic interoperability across disciplines and platforms
- FAIR and machine-actionable data
- Metadata, provenance and persistent identifiers
- Connecting experimental and computational data
- Interoperability between instruments, repositories and research infrastructures
- Semantic annotation and automated data integration
- Data quality, validation and trust
- Standards and community governance
- Preparing materials data for AI and foundation models
- Intellectual property, access and data-sharing models
Session 3 – Description
Lunch
SESSION 4
Software in transition: from AI-Assisted Software Engineering to Sustainable Research Software
Software in transition: from AI-Assisted Software Engineering to Sustainable Research Software
This session will examine how artificial intelligence is changing the way scientific software is designed, developed, tested and maintained. AI-assisted coding tools can accelerate implementation, documentation, debugging and user support, but they also introduce new questions concerning reliability, transparency, security, intellectual property and long-term maintainability.
The session aims to consider scientific software as critical research infrastructure rather than a temporary project output. Particular attention is given to the sustainability of community codes, the growing complexity of software ecosystems, and the skills and governance models needed to maintain trustworthy tools over time. It will discuss how AI may support software modernisation, interoperability and performance portability across rapidly evolving computing architectures..
Topics in scope of the session
- AI-assisted software development and code generation
- Automated testing, debugging and documentation
- Verification and validation of AI-generated code
- Scientific software sustainability and long-term maintenance
- Legacy-code modernisation and technical debt
- Reproducibility, provenance and software citation
- Open-source governance and community development models
- Software quality, security and supply-chain risks
- Interoperability, modularity and reusable software components
- Performance portability across CPUs, GPUs and emerging architectures
- Skills, career paths and recognition for research software engineers
- Licensing, authorship and intellectual-property implications
Session 4 – Description
Chair
Bio
Dr. Natalia Bedoya is a senior scientist at the Materials Center Leoben Forschung GmbH (MCL).
Throughout her scientific career, she has focused on using computer simulations to understand, characterize, and improve the thermal transport properties of materials for technological applications.
Dr. Bedoya obtained her PhD in physics from the Universitat Autònoma de Barcelona. After graduating, she held several postdoctoral positions at various European institutions, including CEA-Saclay, Liphy-Grenoble, Fraunhofer-IWM-Freiburg, and TU-Graz.
In late 2018, she joined MCL as a permanent staff member, focusing on developing numerical methods for thermal transport in nanomaterials. Since 2022, she has led MCL’s software development and data management team. Her team supports developing and integrating stand-alone software tools and digital platforms to accelerate materials development and promote FAIR (Findable, Accessible, Interoperable, Reusable) materials data management.
Natalia Bedoya-Martinez
MCL, Austria
Bio
Ilian Todorov is principal scientific officer at the Science and Technology Facilities Council (UK) where he leads the Computational Chemistry group, based at Daresbury Laboratory.
Ilian Todorov received his first degree in Theoretical Physics in 1996 from the University of Sofia, where he defended his MSc with theoretical work on Hamiltonians to describe collective excitations of atomic nuclei using the Quasiparticle Random Phase Approximation, derived from the time-dependent Hartree-Fock-Bogoliubov equation. After completing his military service in the Bulgarian Air Force in 1997 he was awarded a trans-Europen scholarship (TEPMPUS) at University of Sofia and carried out a research project at the Surface and Colloidal Chemistry Lab at the University of Hull, receiving a joint MSci in Chemistry in 1999. In the same year he was awarded UK’s ORS and University of Bristol scholarships and joined the group of Prof. Neil Allan to specialise in Computational Chemistry methodology. There he studied the thermodynamic properties of ceramics under extreme conditions by using computational techniques such as Quasi-harmonic Lattice Dynamics and Monte Carlo, and also advanced their development by writing software.
After completing his PhD in 2001, Ilian Todorov took the opportunity to join the eMinerals project, led by Prof. Martin Dove at University of Cambridge, as a research assistant and application developer. He was seconded at Daresbury Laboratory where he worked closely with Prof. William Smith on the software development and application of DL_POLY, prior to becoming one of the principal authors of DL_POLY. Ilian Todorov worked on various aspects of HPC technologies related to scalable performance and numerical stability of algorithms for molecular dynamics as well as software interoperability via CML (DL_POLY and SIESTA). Active as Research Software Engineer, he also carried out modelling research with Kostya Trachenko in the area of characterisation of damage in ceramics, glasses and metals subjected to irradiation. Ilian Todorov was actively involved in materials communities such as MCC and CCP5 by contributing talks and associated computational training. In 2007 Ilian Todorov joined Darebusry Laboratory as an HPC expert at Advanced Research Computing group, later joining the Computational Chemistry group in 2010 and then taking the group lead in 2014. In 2017 Ilian Todorov took his Visiting Professor title at Queen Mary University of London in 2017, based on the joint research with his collaborator, Prof. Kostya Trachenko.
Ilian Todorov’s main scientific interests are in the area of soft condensed matter and particularly in modelling and understanding materials behaviour under irradiation in glasses, ceramics and metals. His main technology research interest are in the areas of development of research software for molecular simulations, the interoperability of software in larger infrastructures (ontologies for marketplaces) and the sustained and continual development of the software and the people behind it (Society for RSE, BCS). His group at STFC has developed a number of packages such as DL_POLY, DL_FIELD, DL_MESO, DL_MONTE, ChemShell, etc. and are involved in number of project lines including simulation based research, research software development and marketplace development.Ilian Todorov is the author of about 80 scientific publications.
Ilian Todorov
UKRI, United Kingdom
Plenary 4
SESSION 4
Presentations & Discussion
Software in transition: from AI-Assisted Software Engineering to Sustainable Research Software
This session will examine how artificial intelligence is changing the way scientific software is designed, developed, tested and maintained. AI-assisted coding tools can accelerate implementation, documentation, debugging and user support, but they also introduce new questions concerning reliability, transparency, security, intellectual property and long-term maintainability.
The session aims to consider scientific software as critical research infrastructure rather than a temporary project output. Particular attention is given to the sustainability of community codes, the growing complexity of software ecosystems, and the skills and governance models needed to maintain trustworthy tools over time. It will discuss how AI may support software modernisation, interoperability and performance portability across rapidly evolving computing architectures..
Topics in scope of the session
- AI-assisted software development and code generation
- Automated testing, debugging and documentation
- Verification and validation of AI-generated code
- Scientific software sustainability and long-term maintenance
- Legacy-code modernisation and technical debt
- Reproducibility, provenance and software citation
- Open-source governance and community development models
- Software quality, security and supply-chain risks
- Interoperability, modularity and reusable software components
- Performance portability across CPUs, GPUs and emerging architectures
- Skills, career paths and recognition for research software engineers
- Licensing, authorship and intellectual-property implications
Session 4 – Description
Exhibition & Gold Sponsor Presentations
Poster Prize Award
Social event - Joint dinner
Restaurant Waaiberg
Welcome by EMMC
SESSION 5
Materials industry in transition: Rethinking How Materials Are Developed, Scaled and Commercialised
Materials industry in transition: Rethinking How Materials Are Developed, Scaled and Commercialised
This session will examine how the industrial materials-innovation landscape is being reshaped by digitalisation, artificial intelligence, sustainability targets, supply-chain pressures and new models of collaboration. Materials companies are moving beyond traditional linear research and development towards more connected innovation ecosystems that integrate simulation, data, experimentation, manufacturing and life-cycle considerations.
The session explores how companies are adopting AI-enabled discovery, digital twins, advanced characterisation and automated workflows, while responding to demands for faster scale-up, lower environmental impact and greater resource resilience. It will also consider how roles are changing across industry, start-ups, universities, research infrastructures and technology providers, and what is needed to translate scientific advances into commercially viable materials and processes..
Topics in scope of the session
- AI- and data-enabled industrial materials research
- Digital twins and integrated product–process development
- Accelerating the transition from discovery to scale-up
- Pilot lines, demonstrators and advanced manufacturing
- Sustainable-by-design and circular materials innovation
- Critical raw materials and supply-chain resilience
- Industrial adoption of shared data standards and digital platforms
- Collaboration between industry, academia and research infrastructures
- Start-ups, venture investment and emerging business models
- Intellectual property, data ownership and knowledge sharing
- Regulation, certification and standards for new materials
- Workforce transformation and changing industrial skills
Session 5 – Description
Plenary 5
SESSION 5
Presentations & Discussion
Materials industry in transition: Rethinking How Materials Are Developed, Scaled and Commercialised
This session will examine how the industrial materials-innovation landscape is being reshaped by digitalisation, artificial intelligence, sustainability targets, supply-chain pressures and new models of collaboration. Materials companies are moving beyond traditional linear research and development towards more connected innovation ecosystems that integrate simulation, data, experimentation, manufacturing and life-cycle considerations.
The session explores how companies are adopting AI-enabled discovery, digital twins, advanced characterisation and automated workflows, while responding to demands for faster scale-up, lower environmental impact and greater resource resilience. It will also consider how roles are changing across industry, start-ups, universities, research infrastructures and technology providers, and what is needed to translate scientific advances into commercially viable materials and processes..
Topics in scope of the session
- AI- and data-enabled industrial materials research
- Digital twins and integrated product–process development
- Accelerating the transition from discovery to scale-up
- Pilot lines, demonstrators and advanced manufacturing
- Sustainable-by-design and circular materials innovation
- Critical raw materials and supply-chain resilience
- Industrial adoption of shared data standards and digital platforms
- Collaboration between industry, academia and research infrastructures
- Start-ups, venture investment and emerging business models
- Intellectual property, data ownership and knowledge sharing
- Regulation, certification and standards for new materials
- Workforce transformation and changing industrial skills
Session 5 – Description
Lunch
SESSION 6
Infrastructure in transition: Building sustainable digital materials infrastructures for research and industry
Infrastructure in transition: Building sustainable digital materials infrastructures for research and industry
This session will address how digital materials-research infrastructures can move from individual projects and fragmented platforms towards trusted, interoperable and sustainable services for the wider research and innovation community. It will combine European policy and implementation perspectives with practical experience from international materials-data and automation initiatives.
A central focus is the development of the European Materials Commons: a federated digital infrastructure intended to connect materials data, models, workflows and tools across research and industry. The European Commission’s advanced-materials agenda identifies the Materials Commons as a long-term digital infrastructure, while the Horizon Europe work programme calls for the demonstration of a pioneering federated infrastructure through practical use cases.
The session will look beyond initial technical development to consider what makes an infrastructure genuinely sustainable: stable funding, long-term institutional responsibility, community governance, interoperability, reliable software maintenance, skills, user support and environmentally responsible computing. Contributions from Japan, South Korea and Canada provide valuable comparisons between different national strategies, governance arrangements and models of service provision.
Topics in scope of the session
- European policy and roadmap for the Materials Commons
- From funded projects to persistent research services
- Federated architectures and interoperability between platforms
- Governance, ownership and institutional responsibility
- Sustainable funding and operational business models
- Long-term software, data and standards maintenance
- Connections with European research infrastructures, data spaces and EOSC
- Data sovereignty, cybersecurity and trusted access
- Balancing openness with intellectual property and industrial confidentiality
- Skills, user support and community engagement
- Environmental sustainability of data- and compute-intensive infrastructures
- International interoperability and reciprocal access