This workshop examines how research policies at national, regional, and international levels shape scientific priorities, funding allocation, and long-term innovation trajectories. As research systems become increasingly mission-oriented, alignment between policy objectives, societal challenges, and scientific excellence is essential to maximise impact and maintain academic freedom.

The session explores the evolution of research policy instruments, including framework programmes, mission-driven research agendas, and public–private partnerships. Particular attention is given to how policy frameworks influence interdisciplinary research, international collaboration, and the translation of research into societal value.

Academic contributions will analyse policy design, evaluation methodologies, and unintended consequences of top-down research steering. Industry and policy speakers will share perspectives on how research policy affects innovation ecosystems, competitiveness, and long-term investment decisions.

The workshop aims to provide researchers, research managers, and policymakers with tools to navigate policy landscapes while safeguarding scientific integrity.

This workshop examines access to, governance of, and control over research infrastructures, which are increasingly central to scientific excellence, innovation capacity, and geopolitical competitiveness. Research infrastructures today extend beyond traditional laboratories and experimental facilities to include digital platforms, large-scale databases, high-performance computing systems, testbeds, and shared experimental environments. As their complexity, cost, and strategic importance grow, questions of ownership, access rights, governance models, and long-term sustainability become critical.

The workshop explores how access to infrastructure shapes research agendas, collaboration patterns, and power dynamics within and between research systems. Particular attention is given to disparities in access across institutions, regions, and disciplines, as well as the implications for early-career researchers and institutions with limited resources. The role of international research infrastructures and transnational access schemes is discussed in the context of scientific openness, security considerations, and strategic autonomy.

Academic contributions will analyse governance models, access frameworks, and incentive structures for shared infrastructures, drawing on case studies from large-scale facilities and digital research platforms. Industry and infrastructure operators will provide insights into operational realities, investment decisions, access prioritisation, and public–private models for infrastructure development and management.

The workshop aims to equip researchers, research managers, policymakers, and infrastructure operators with practical frameworks for ensuring fair, efficient, and sustainable access to research infrastructures while balancing openness, security, and strategic interests.

This workshop examines the role of intellectual property (IP) and patenting within contemporary research ecosystems, where the creation, dissemination, and commercialisation of knowledge increasingly intersect. As universities, public research organisations, and industry partners collaborate more closely, the management of intellectual property has become a central strategic concern, influencing innovation pathways, research incentives, and academic careers.

The workshop explores how patent systems and IP policies shape research behaviour, collaboration dynamics, and the translation of scientific discoveries into societal and economic value. Particular attention is given to the tensions between open science principles and the protection of commercially valuable knowledge, as well as differences in IP cultures across disciplines and regions. The implications of IP strategies for early-stage research, interdisciplinary collaboration, and international partnerships are examined in depth.

Academic contributions will address the economic, legal, and organisational dimensions of intellectual property, including patent quality, disclosure timing, and the impact of IP regimes on scientific progress. Industry and technology transfer experts will provide practical insights into licensing strategies, spin-off creation, and negotiation processes, highlighting common pitfalls and success factors in managing research-derived intellectual property.

The workshop is designed to help researchers, technology transfer professionals, research managers, and policymakers develop informed IP strategies that support innovation, collaboration, and long-term knowledge creation without undermining scientific openness.

This workshop focuses on education, training, and skills development as foundational pillars for a resilient and competitive research ecosystem. As research systems face increasing complexity, interdisciplinarity, and pressure to deliver societal impact, traditional academic training models are no longer sufficient. Researchers must be equipped not only with deep disciplinary expertise, but also with transversal skills including data literacy, collaboration across sectors, project leadership, ethics, communication, and innovation management.

The workshop examines how education systems at undergraduate, graduate, and postdoctoral levels can evolve to better prepare researchers for diverse career paths across academia, industry, policy, and entrepreneurship. Particular attention is given to the mismatch between current training structures and actual career outcomes, as well as to the role of lifelong learning in rapidly evolving research and technology landscapes.

Academic speakers will present evidence on skills gaps, curriculum innovation, and pedagogical models that foster creativity, critical thinking, and responsible research practices. Industry and policy speakers will highlight workforce needs, expectations for research-trained professionals, and successful models of academia–industry co-designed education. The workshop also addresses inclusivity, international mobility, and equitable access to high-quality research training.

The goal of the workshop is to develop actionable recommendations for universities, funding agencies, research organisations, and policymakers to modernise research education and build sustainable human capital for the future.

This workshop focuses on identifying, assessing, and anticipating emerging research topics that are likely to shape scientific, technological, and societal landscapes over the next decade. In an environment characterised by rapid technological change, geopolitical uncertainty, and evolving societal needs, strategic foresight has become an essential capability for research institutions, funding agencies, and policymakers.

The workshop explores methodologies for horizon scanning, weak-signal detection, and scenario building, and how these tools can be integrated into research strategy, funding priorities, and institutional planning. It examines how emerging topics move from early-stage exploration to mainstream research agendas, and how premature lock-in or delayed investment can hinder innovation and impact.

Academic speakers will present foresight frameworks, bibliometric and data-driven approaches, and case studies where early identification of emerging fields led to scientific or economic leadership. Industry and policy perspectives will address how foresight informs R&D investment decisions, portfolio management, and long-term competitiveness. Special attention is given to interdisciplinarity, convergence research, and the role of societal challenges in shaping future research directions.

The workshop aims to equip participants with practical tools to anticipate change, manage uncertainty, and align research agendas with long-term scientific excellence and societal value.

This workshop addresses data as a central asset of modern research systems, underpinning scientific discovery, innovation, and evidence-based decision-making. As research becomes increasingly data-intensive, questions surrounding how data are produced, curated, shared, reused, and protected have moved to the forefront of research governance and practice. Ensuring that research data are accessible, trustworthy, interoperable, and secure is essential for scientific integrity, reproducibility, and societal impact.

The workshop explores the full research data lifecycle, from data generation and quality assurance to storage, sharing, and long-term preservation. It examines the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) data principles alongside legal, ethical, and security constraints, including data protection, intellectual property, and sensitive data handling. Particular attention is paid to the tensions between open science objectives and legitimate needs for data control, confidentiality, and strategic protection.

Academic contributions will address data standards, metadata, reproducibility, and emerging practices in open and computational science. Industry and policy speakers will highlight perspectives on data governance, trusted data spaces, cross-border data sharing, and the protection of commercially or strategically sensitive information. The workshop also considers the skills, infrastructure, and incentives required to make high-quality data stewardship a routine part of research culture.

The aim of the workshop is to develop a shared understanding of responsible data practices and to provide actionable guidance for researchers, institutions, funders, and policymakers navigating the evolving data landscape.

This workshop focuses on how research knowledge is translated into societal, economic, and technological value through effective technology transfer and knowledge valorisation. While high-quality research is a necessary foundation, impact is not automatic; it requires deliberate mechanisms, skills, incentives, and institutional support to move ideas from laboratories into real-world applications.

The workshop examines the full valorisation pathway, including intellectual property management, licensing strategies, spin-off creation, collaborative research with industry, and non-commercial forms of impact such as policy advice, standards, and social innovation. It highlights the diversity of valorisation models across disciplines, sectors, and regions, and addresses the persistent gap between academic incentives and impact-oriented activities.

Academic speakers will discuss evidence on what drives successful knowledge transfer, the role of researchers at different career stages, and how institutional frameworks shape behaviour. Industry and innovation ecosystem representatives will share practical perspectives on engaging with academia, de-risking early-stage technologies, and building long-term partnerships. The workshop also explores how funding schemes, evaluation criteria, and career assessment systems can better recognise and reward valorisation activities.

The objective of the workshop is to identify effective strategies for enabling responsible and sustainable technology transfer while preserving academic freedom and research excellence.

This workshop examines the evolving relationship between academia and industry and the cultural, structural, and incentive-related differences that shape collaboration between the two. While academic and industrial research increasingly intersect, they operate under distinct norms regarding timelines, risk tolerance, evaluation criteria, and definitions of success. Understanding and managing these differences is essential for effective collaboration and mutual benefit.

The workshop explores how roles and expectations differ across sectors, how incentives influence behaviour, and how misalignment can hinder collaboration or distort research agendas. It also examines hybrid models such as industrial PhDs, joint research centres, and public–private partnerships that seek to bridge cultural divides.

Academic speakers will analyse institutional logics, reward systems, and career structures in academia, while industry representatives will describe R&D priorities, decision-making processes, and collaboration constraints. The discussion will address ethical considerations, conflicts of interest, and the preservation of academic independence. The workshop aims to provide practical guidance for designing collaborations that respect sectoral differences while maximising scientific and societal value.

This workshop explores the transformative role of artificial intelligence (AI) across the research lifecycle, from hypothesis generation and data analysis to peer review, evaluation, and research management. AI offers unprecedented opportunities to accelerate discovery, enhance reproducibility, and improve decision-making, while simultaneously raising concerns about transparency, bias, accountability, and skills displacement.

The workshop examines how AI tools are reshaping research practices across disciplines, how institutions can responsibly adopt AI systems, and how governance frameworks must evolve to address ethical and societal implications. It also considers the impact of AI on research evaluation, funding allocation, and career progression.

Academic speakers will present advances in AI-driven science and meta-research, while industry and policy speakers will discuss implementation challenges, governance models, and responsible AI principles. The workshop aims to move beyond hype toward realistic, evidence-based integration of AI into research systems.

This workshop addresses the evolving role of research publications and outputs in shaping careers, evaluation, and the broader purpose of research. While publications remain central to academic recognition, increasing emphasis on open science, data sharing, societal impact, and diverse output formats challenges traditional notions of success and merit.

The workshop examines how publication practices influence research behaviour, career trajectories, and knowledge dissemination. It explores tensions between quality and quantity, openness and competition, and academic freedom and performance metrics. Particular attention is given to early-career researchers, whose careers are often most affected by publication norms and evaluation criteria.

Academic speakers will present evidence on publishing systems, peer review, and research assessment reform. Publishers, funders, and policy representatives will discuss new models of dissemination, incentives, and evaluation. The workshop aims to clarify the purpose of research outputs and align publishing practices with scientific integrity, career sustainability, and societal value.

This workshop focuses on research funding as a central structuring force of the research ecosystem, shaping priorities, behaviours, collaboration patterns, and career trajectories. Funding schemes differ widely in objectives, scope, funding rates, risk tolerance, and expectations regarding impact, yet researchers and institutions often struggle to navigate this landscape strategically.

The workshop examines how different funding instruments support basic versus applied research, individual versus collaborative projects, and academic versus industry-led initiatives. It explores how funding rates, cost models, and administrative requirements influence research design and participation, and how strategic alignment between research goals and funding schemes can significantly improve success rates and outcomes.

Academic speakers will analyse how funding structures shape research agendas and scientific risk-taking, while policy and funding agency representatives will explain the rationale behind different instruments and evaluation criteria. Industry perspectives will address co-funded research, public–private partnerships, and expectations for deliverables and timelines. The workshop also provides guidance on selecting appropriate funding schemes at different career stages and for different types of research ambition.

The objective of the workshop is to equip participants with a strategic understanding of funding landscapes and to clarify success criteria for competitive, responsible, and impactful research funding.

Artificial intelligence is fundamentally transforming materials science by enabling predictive modeling, inverse design, and automated discovery of novel material compositions. Generative AI models, high-throughput simulations, and machine learning algorithms are significantly shortening research and development cycles across sectors including energy storage, aerospace, advanced polymers, and semiconductors.

This workshop explores AI-driven materials informatics, data infrastructure, simulation-to-experiment integration, and closed-loop laboratory automation. Participants will examine how generative models can predict material properties, optimize compositions, and reduce costly experimental iterations.

Academic experts will present advances in inverse materials design and AI-enabled discovery platforms. Industry leaders will demonstrate real-world deployment in battery technology, alloys, and specialty chemicals. Policy representatives will address data governance, transparency, and ethical AI frameworks in scientific research.

The goal of this workshop is to equip materials scientists, R&D directors, and innovation strategists with actionable approaches to integrate AI into advanced materials development pipelines.

The circular economy requires materials intentionally engineered for reuse, recyclability, and biodegradability. From advanced polymer depolymerization to recyclable metal alloys and modular construction systems, circular material design is redefining sustainability in manufacturing.

This workshop explores eco-design methodologies, lifecycle assessment tools, chemical and mechanical recycling technologies, and policy frameworks supporting closed-loop production systems.

Academic experts will present lifecycle modeling and circular design principles. Industry leaders will showcase advanced recycling infrastructure and product redesign strategies. Policy representatives will discuss regulatory drivers and international harmonization efforts.

The objective is to enable industry and policymakers to integrate circularity at the design phase and develop resilient, resource-efficient material systems.

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Mycelium-based materials are biodegradable, fire-resistant, and carbon-negative alternatives for insulation, packaging, and structural components. By controlling fungal growth within agricultural waste substrates, scalable biofabrication processes can produce lightweight, sustainable materials with low embodied carbon.

This workshop explores fungal growth engineering, mechanical performance testing, architectural integration, and commercialization strategies.

The objective is to accelerate the mainstream adoption of fungal biomaterials in construction and packaging industries.

Self-healing materials extend infrastructure lifespan by autonomously repairing cracks and damage through embedded bacteria, microcapsules, or reversible chemical bonds. These technologies reduce maintenance costs, enhance resilience, and support sustainable infrastructure systems.

This workshop explores biological and chemical repair mechanisms, durability testing, infrastructure case studies, and certification frameworks.

The objective is to accelerate industrial deployment of autonomous repair technologies.

Replacing fossil-based materials with renewable alternatives is critical to decarbonizing construction and manufacturing. Hempcrete, engineered bamboo, biochar-based composites, and agricultural byproduct materials offer structural integrity while sequestering carbon.

This workshop explores renewable feedstocks, structural performance, carbon accounting methodologies, and supply chain development. Participants will examine how bio-based materials can meet building standards while supporting regenerative land use practices.

Academic researchers will present material performance data and carbon sequestration metrics. Industry leaders will share construction case studies. Policy representatives will address building code adaptation and green procurement frameworks.

The objective is to accelerate mainstream adoption of renewable construction materials.

Engineered Living Materials integrate biological organisms such as bacteria or algae into structural materials capable of oxygen production, carbon capture, or self-repair. This convergence of synthetic biology and materials science opens pathways toward adaptive, regenerative infrastructure.

The workshop examines containment strategies, biosafety protocols, performance optimization, and environmental impact assessment.

The goal is to explore safe, scalable, and regulated deployment of living materials.

Structural battery composites combine mechanical strength and electrical energy storage in a single multifunctional material. By integrating carbon fiber electrodes and solid electrolytes into load-bearing structures, these materials can reduce vehicle weight and increase electric vehicle range.

This workshop explores electrochemical performance, mechanical trade-offs, safety standards, and automotive integration strategies.

The goal is to accelerate adoption of multifunctional energy materials.

Transparent wood and advanced bio-based glass alternatives are redefining architectural materials by combining optical transparency with thermal insulation and reduced carbon footprint. Through lignin removal and polymer infiltration, wood can be transformed into a lightweight, shatter-resistant, and thermally efficient alternative to glass.

This workshop explores processing technologies, optical and mechanical performance, façade integration, and lifecycle sustainability metrics. Participants will examine the potential of transparent wood in energy-efficient buildings and climate-adaptive architecture.

Academic experts will present breakthroughs in nanostructured wood modification. Industry leaders will showcase pilot construction projects. Standards representatives will discuss compliance with building codes and fire regulations.

The goal is to accelerate adoption of bio-based transparent materials in mainstream construction.

Shape memory alloys and polymers enable structures that respond to temperature, light, or electrical stimuli by returning to pre-programmed forms. These materials are enabling adaptive façades, deployable aerospace components, and minimally invasive biomedical devices.

This workshop explores activation mechanisms, fatigue performance, system integration, and manufacturing scalability. Participants will evaluate industrial deployment pathways and reliability testing frameworks.

The objective is to bridge fundamental materials science with real-world adaptive systems.

Nanozymes are nanostructured materials with enzyme-like catalytic activity. They offer greater stability, lower cost, and enhanced durability compared to natural enzymes, with applications in diagnostics, targeted therapy, and environmental cleanup.

This workshop explores catalytic mechanisms, biomedical applications, toxicity assessment, and environmental deployment strategies.

The objective is to translate laboratory breakthroughs into scalable healthcare and environmental solutions.

Graphene and other 2D materials exhibit extraordinary electrical conductivity, mechanical strength, and flexibility. Advances in scalable 3D printing and roll-to-roll manufacturing are enabling commercial deployment in sensors, batteries, flexible electronics, and aerospace systems.

The workshop explores synthesis methods, large-scale production, electronic applications, and commercialization pathways.

Phase-change materials store and release thermal energy during phase transitions, improving insulation and enabling passive temperature regulation. Advanced salt hydrates, bio-based PCMs, and aerogel composites are transforming energy-efficient building systems.

The workshop addresses material chemistry, integration into HVAC systems, and carbon reduction strategies.

Beyond insulation, advanced aerogels—synthetic polymer and bio-based—are being developed for tissue scaffolds, drug delivery, filtration systems, and environmental remediation.

This workshop explores fabrication techniques, biomedical integration, and industrial deployment.

Marine-derived materials such as algae fibers, seaweed composites, and mussel-shell biominerals offer biodegradable and carbon-sequestering alternatives for construction, textiles, and packaging.

This workshop explores biofabrication from marine biomass, structural performance, and supply chain scalability.

The objective is to unlock the potential of ocean-based resources in sustainable material systems.

Additive manufacturing is evolving beyond prototyping into a platform for producing complex, multi-functional components with embedded intelligence and performance capabilities. The integration of robotic arms, multi-axis printing systems, and smart materials enables the fabrication of load-bearing, conductive, responsive, and graded-material structures in a single production process.

This workshop explores large-scale robotic additive manufacturing, multi-material deposition, embedded electronics, and programmable matter. Participants will examine how smart materials such as conductive polymers, shape-memory alloys, and fiber-reinforced composites are being integrated into advanced production workflows.

Academic experts will present research on robotic fabrication systems and digital material design. Industry leaders will showcase aerospace, automotive, and construction applications. Standards and policy representatives will address qualification, certification, and supply chain transformation.

The goal of this workshop is to equip engineers, manufacturers, and innovation leaders with strategies to deploy robotic additive systems for scalable, on-demand production of functional components.

Regenerative design moves beyond sustainability toward actively restoring ecosystems and improving environmental systems. Materials such as biochar composites, algae-based biopolymers, carbon-sequestering concrete, and living building materials are enabling structures that capture carbon, regenerate soil, and enhance biodiversity.

This workshop explores regenerative material strategies, carbon accounting methodologies, ecosystem integration, and policy alignment. Participants will examine how material systems can contribute to climate-positive construction and circular bioeconomies.

Academic experts will present frameworks for regenerative architecture and carbon sequestration science. Industry leaders will showcase regenerative building projects and biochar-enhanced construction systems. Policy representatives will address carbon markets, environmental certification, and global climate alignment.

The goal of this workshop is to provide engineers, architects, policymakers, and investors with actionable pathways to implement regenerative material systems at scale.

Additive manufacturing (AM) is transitioning from a prototyping tool to a core industrial production technology. This workshop focuses on the acceleration toward high-volume manufacturing, driven by multi-laser metal systems, large-format printing platforms, and automation-ready production lines.

The sub-workshop explores advanced metal powder bed fusion (PBF-LB/M), directed energy deposition (DED), binder jetting, and large-scale polymer and composite printing systems. Key topics include productivity enhancement, process stability, quality assurance, digital workflow integration, and supply-chain readiness for serial production.

Presentations will address technological breakthroughs enabling faster build rates, multi-laser synchronization, in-situ monitoring, and AI-driven process control. Challenges related to certification, repeatability, post-processing, and workforce qualification will also be examined. Industry leaders will share practical insights from scaling AM to industrial throughput levels in aerospace, automotive, energy, and medical sectors.

The workshop aims to provide a comprehensive understanding of technology maturity, production economics, and system integration strategies necessary to move additive manufacturing beyond prototyping toward fully industrialized, high-volume production.

This workshop explores AI-driven materials informatics, data infrastructure, simulation-to-experiment integration, and closed-loop laboratory automation. Participants will examine how generative models can predict material properties, optimize compositions, and reduce costly experimental iterations.

Academic experts will present advances in inverse materials design and AI-enabled discovery platforms. Industry leaders will demonstrate real-world deployment in battery technology, alloys, and specialty chemicals. Policy representatives will address data governance, transparency, and ethical AI frameworks in scientific research.

The goal of this workshop is to equip materials scientists, R&D directors, and innovation strategists with actionable approaches to integrate AI into advanced materials development pipelines.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming additive manufacturing (AM) from a parameter-driven process into an adaptive, data-driven production system. This workshop focuses on the integration of AI across the AM value chain—design optimisation, print-path generation, real-time anomaly detection, predictive maintenance, and closed-loop process control—enabling the production of “born-qualified” parts.

The sub-workshop explores AI-enhanced metal powder bed fusion (PBF), directed energy deposition (DED), binder jetting, and polymer AM systems. Key topics include in-situ monitoring, sensor fusion, digital twins, data infrastructure, and explainable AI for certification environments. Presentations will address recent advances in deep learning for defect prediction, adaptive scan strategies, and automated quality assurance.

Speakers from academia will present state-of-the-art research in data-driven modelling and intelligent control systems, while industry leaders will share practical experience implementing AI solutions in serial production environments. Particular emphasis will be placed on qualification-by-design, reduced inspection costs, and accelerated certification pathways.

The workshop aims to provide a comprehensive understanding of how AI integration can enhance productivity, reliability, and scalability in additive manufacturing, supporting the transition toward autonomous, smart AM factories.

Sustainability is becoming a central driver in the evolution of additive manufacturing (AM), with increasing emphasis on reducing environmental impact across materials sourcing, production processes, and end-of-life management. This workshop focuses on the integration of recycled powders, bio-based and biodegradable filaments, low-carbon alloys, and circular production strategies to enable greener additive manufacturing ecosystems.

The sub-workshop explores sustainable innovations across metal powder bed fusion (PBF), directed energy deposition (DED), binder jetting, and polymer extrusion technologies. Key topics include lifecycle assessment (LCA), powder recyclability and degradation, material traceability, energy consumption reduction, and closed-loop material systems. Presentations will examine scientific advances in recycled feedstock qualification, bio-based polymer development, and design-for-circularity principles.

Academic contributions will highlight material performance, durability, and environmental benchmarking, while industry speakers will provide real-world insights into sustainable supply chains, certification of recycled materials, and carbon footprint reduction strategies. Special attention will be given to regulatory drivers, ESG requirements, and the economic case for sustainable AM adoption.

The workshop aims to provide a comprehensive understanding of how additive manufacturing can contribute to climate goals, resource efficiency, and circular economy models while maintaining industrial performance and scalability.

Multi-material additive manufacturing (AM) represents a major technological leap toward functional integration and design freedom, enabling the fabrication of components that combine distinct materials—such as hard and soft polymers, metals and ceramics, or conductive and insulating materials—within a single build process.

This workshop focuses on the development of advanced systems capable of depositing, fusing, or co-processing multiple materials in a controlled and repeatable manner. The sub-workshop explores hybrid powder bed fusion, material jetting, directed energy deposition (DED), binder jetting, and extrusion-based technologies designed for multi-material capability.

Key topics include interfacial bonding mechanisms, thermal compatibility, residual stress management, process synchronisation, and digital workflow adaptation for heterogeneous materials. Presentations will examine recent advances in graded materials, embedded electronics, structural-functional integration, and bio-inspired architectures. Technical challenges such as material cross-contamination, qualification standards, and certification pathways will also be addressed.

Academic speakers will present cutting-edge research on multi-material interfaces and graded structures, while industry leaders will share insights into real-world implementation in aerospace, medical devices, automotive lightweighting, and electronics integration.

The workshop aims to provide a comprehensive understanding of the scientific, technical, and industrial foundations required to scale multi-material additive manufacturing toward serial production and high-value applications.

Distributed and on-demand additive manufacturing (AM) is reshaping global production models by enabling localized manufacturing, reducing supply-chain dependencies, and introducing the concept of “digital inventory.” Instead of storing physical spare parts, companies can store certified digital files and produce components only when and where they are needed.

This workshop explores the technological, logistical, and regulatory foundations required to implement decentralized AM networks. The sub-workshop covers secure data transmission, digital part certification, cloud-based production management, standardized qualification protocols, and integration with existing supply chains.

Key topics include digital thread implementation, cybersecurity for design files, distributed quality assurance, remote machine monitoring, and the economics of local versus centralized production. Presentations will address how AM can enhance supply-chain resilience, reduce lead times, minimize warehousing costs, and lower carbon emissions associated with transportation.

Academic speakers will present research on distributed manufacturing systems, digital twins, and secure design frameworks, while industry leaders will share real-world case studies in aerospace, defense, energy, healthcare, and spare-parts logistics.

The workshop aims to provide a comprehensive understanding of how additive manufacturing enables flexible, resilient, and sustainable production ecosystems through digitalization and localized fabrication.

Advanced metal additive manufacturing (AM) technologies are rapidly evolving to enable cost-competitive, high-throughput production of structural and functional metal components. This workshop focuses on the expansion of metal binder jetting, Directed Energy Deposition (DED), and metal filament extrusion as scalable alternatives to traditional laser powder bed fusion systems, with the objective of reducing part costs and expanding industrial adoption.

The sub-workshop explores high-speed binder jetting with sintering, wire- and powder-based DED systems, and extrusion-based metal printing (bound metal deposition). Key topics include densification strategies, microstructural control, material qualification, post-processing optimisation, and integration into hybrid manufacturing environments.

Presentations will examine productivity gains, material efficiency, and energy consumption, alongside challenges such as shrinkage control, surface finish, dimensional accuracy, and certification for critical industries. Academic speakers will highlight advances in metallurgy, sintering science, and process modelling, while industry leaders will provide insights into scaling production lines, reducing cost-per-part, and achieving serial manufacturing readiness.

The workshop aims to provide a comprehensive understanding of emerging cost-efficient metal AM technologies and their role in transforming metal component production across automotive, aerospace, tooling, and energy sectors.

As additive manufacturing (AM) scales toward serial production, automation of the full workflow—particularly post-processing—has become essential to achieving industrial productivity, cost efficiency, and repeatable quality. This workshop focuses on automated depowdering, support removal, heat treatment handling, surface finishing, inspection, and integrated robotic material handling systems.

The sub-workshop explores end-to-end automation strategies across metal and polymer AM platforms, including robotic powder recovery, automated unpacking stations, CNC hybrid finishing, surface treatment cells, and AI-enabled inspection systems. Emphasis will be placed on reducing manual intervention, improving worker safety, minimizing variability, and increasing throughput.

Key topics include robotic integration, machine-to-machine communication, closed-loop production cells, inline metrology, and digital production tracking. Presentations will address current bottlenecks in post-processing—often representing the majority of total production time and cost—and strategies to transform AM into a fully automated, lights-out manufacturing process.

Academic speakers will present advances in robotic manipulation, intelligent finishing systems, and automated quality control, while industry leaders will share case studies of fully integrated AM production lines.

The workshop aims to provide a comprehensive understanding of how automation and robotics can unlock the productivity gains required for high-volume additive manufacturing.

Design for Additive Manufacturing (DfAM) is a critical enabler for unlocking the full potential of additive manufacturing (AM). Moving beyond conventional design adaptation, DfAM leverages generative design, topology optimization, lattice structures, and biomimetic engineering to create lightweight, high-performance, and functionally integrated components

that cannot be produced using traditional manufacturing methods.

This workshop focuses on advanced engineering methodologies and digital design tools that maximize the benefits of AM, including weight reduction, part consolidation, performance optimization, and material efficiency. The sub-workshop explores computational design algorithms, multi-physics simulation, AI-assisted design exploration, and design automation workflows.

Key topics include topology optimization for load-bearing structures, lattice and metamaterial architectures, thermal-fluid optimization, design for multi-material systems, and qualification-by-design principles. Presentations will also address the integration of DfAM into industrial product development cycles and certification processes in regulated sectors such as aerospace, automotive, and medical devices.

Academic speakers will highlight breakthroughs in computational design and simulation-driven optimization, while industry leaders will present real-world case studies demonstrating performance gains, cost reductions, and accelerated innovation cycles enabled by DfAM.

The workshop aims to provide a comprehensive understanding of advanced design methodologies that enable additive manufacturing to deliver superior technical performance and economic value.

4D printing represents the next frontier of additive manufacturing (AM), integrating smart materials that can change shape, properties, or functionality over time in response to external stimuli such as heat, moisture, light, magnetic fields, or electricity. By combining advanced materials science with programmable design, 4D printing enables adaptive, self-assembling, and multifunctional structures.

This workshop focuses on shape-memory polymers and alloys, hydrogels, electroactive materials, magneto-responsive composites, and stimuli-responsive ceramics. The sub-workshop explores design strategies for time-dependent behavior, multi-material deposition for programmable responses, and modeling approaches for predicting structural transformation.

Key topics include material formulation, actuation mechanisms, durability and fatigue under cyclic stimuli, embedded sensing, and integration of responsive systems into real-world applications. Presentations will examine recent scientific breakthroughs in programmable matter, bio-inspired structures, soft robotics, and adaptive aerospace and biomedical components.

Academic speakers will present cutting-edge research on smart materials and responsive architectures, while industry representatives will discuss commercialization pathways, reliability testing, and application-driven development in sectors such as healthcare, aerospace, robotics, and wearable technologies.

The workshop aims to provide a comprehensive understanding of the technological foundations, industrial potential, and commercialization challenges of 4D printing and smart material systems.

Additive manufacturing (AM), commonly known as 3D printing, is transforming how products are designed, prototyped, and produced. Yet, when combined with traditional subtractive techniques like CNC milling, drilling, or turning in a hybrid manufacturing setup, it offers unprecedented precision, surface finish quality, and functional complexity.

This workshop explores the integration of additive and subtractive processes in single platforms (“hybrid manufacturing”), examining the advantages, limitations, and practical considerations for industries ranging from aerospace and automotive to medical devices. Participants will gain insight into materials selection, design optimization, process control, and quality assurance in hybrid systems.

Academic experts will provide comparative analyses of AM techniques (metal, polymer, and composite), lifecycle assessment (LCA), and economic viability. Industry leaders will showcase case studies demonstrating real-world implementation of hybrid manufacturing, including high-precision components and complex geometries. Policy and standardization representatives will discuss certification frameworks, cross-industry harmonization, and industrial strategy for advanced manufacturing technologies.

The objective of this workshop is to provide clarity on the integration of additive and subtractive processes, enabling engineers, manufacturers, investors, and policymakers to make informed decisions regarding adoption, scalability, and strategic deployment.

Additive manufacturing (AM) is revolutionizing healthcare by enabling personalized medical implants, dental devices, prosthetics, and tissue engineering. Bioprinting—the 3D printing of living cells and biomaterials—is opening new frontiers in regenerative medicine, organ fabrication, and drug testing platforms.

This workshop provides a comprehensive overview of medical and bioprinting innovations, examining technological capabilities, regulatory frameworks, clinical applications, and commercialization pathways. Participants will explore the scientific, engineering, and ethical considerations of printing patient-specific implants, scaffolds, and tissues.

Academic speakers will present cutting-edge research in biomaterials, scaffold design, cell viability, and tissue maturation. Industry experts will demonstrate real-world deployment of 3D-printed implants, dental solutions, and surgical guides. Regulatory representatives will discuss approval pathways, standards for patient safety, and harmonization of international guidelines.

The objective of this workshop is to equip clinicians, engineers, researchers, investors, and policymakers with actionable knowledge about the opportunities, challenges, and strategic considerations for adopting additive and bioprinting technologies in medicine.

As additive and hybrid manufacturing becomes more digitally integrated, safeguarding intellectual property (IP) and ensuring data traceability throughout the production lifecycle are critical. The concept of the “digital thread” links design, simulation, production, and inspection, creating a continuous, auditable flow of information.

This workshop explores strategies for securing proprietary designs, managing sensitive manufacturing data, and maintaining regulatory and quality compliance. Participants will learn about digital twins, blockchain-enabled traceability, cybersecurity protocols, and standards for IP protection in advanced manufacturing environments.

Academic experts will present frameworks for data governance, traceability, and cybersecurity in industrial AM workflows. Industry leaders will showcase real-world solutions for protecting designs and ensuring secure production pipelines. Policy and standards representatives will discuss regulatory expectations, international harmonization, and best practices for safeguarding digital manufacturing ecosystems.

The goal of this workshop is to equip engineers, IT professionals, industrial managers, and policymakers with actionable strategies to protect sensitive manufacturing data, maintain compliance, and enhance trust in digitally connected production networks.

Large-Format Additive Manufacturing (LFAM) is revolutionizing production by enabling the fabrication of massive components that were previously impossible or cost-prohibitive with traditional methods. LFAM is applied in aerospace for structural components, in automotive for tooling and vehicle parts, and in construction for entire building segments.

This workshop explores the technical, operational, and strategic aspects of LFAM. Participants will gain insights into specialized printing systems, material handling, process optimization, quality control, and integration with downstream manufacturing. Case studies will illustrate how LFAM reduces lead times, lowers costs, and enables innovative design possibilities.

Academic speakers will cover large-scale material behavior, process modeling, and structural performance. Industry experts will present real-world applications and deployment strategies. Policy and standards representatives will discuss certification, safety, and quality standards for large-scale additive production.

The objective of this workshop is to equip engineers, designers, industrial managers, and policymakers with actionable knowledge for adopting LFAM at scale, addressing both technical and strategic challenges.

Widespread adoption of additive manufacturing (AM) requires formal standards and certification frameworks for materials, processes, and end-use parts. Standardization ensures quality, safety, reproducibility, and interoperability across industries, enabling manufacturers to confidently scale AM technologies from prototyping to full-scale production.

This workshop explores the development and implementation of standards for additive manufacturing, including material specifications, process validation, quality assurance, and regulatory compliance. Participants will gain insights into international harmonization efforts, certification pathways, and best practices for industrial adoption.

Academic experts will discuss technical requirements, material characterization, and process validation methodologies. Industry leaders will present case studies of certified additive components and industrial deployment. Policy and standards representatives will address ISO, ASTM, and other international frameworks for AM standardization and certification.

The goal of this workshop is to provide engineers, quality managers, industrial strategists, and policymakers with the knowledge needed to implement standardized, certified AM practices and accelerate adoption across sectors.

Additive manufacturing is evolving beyond structural parts to include functional components with embedded electronics. This includes conductive traces, sensors, actuators, and circuits integrated directly into the geometry during printing. Such innovations enable lightweight, compact, and multifunctional devices across aerospace, automotive, robotics, and IoT applications.

This workshop explores the technologies, materials, and design strategies required to fabricate functional parts with embedded electronics. Participants will learn about multi-material printing, sensor integration, electrical performance, and process optimization for hybrid functional components. Regulatory considerations, testing methods, and industrial adoption challenges will also be discussed.

Academic experts will present advances in conductive inks, hybrid printing methods, and functional device design. Industry leaders will showcase real-world applications and implementation strategies. Policy and standards representatives will address quality assurance, certification, and IP protection for electrically functional parts.

The goal of this workshop is to provide engineers, designers, researchers, and industrial decision-makers with actionable knowledge on integrating electronics into 3D-printed components for next-generation multifunctional devices.

Hydrogen is often discussed as a single energy carrier, yet its climate impact, economic viability, and strategic role depend heavily on its production pathway. “Green” hydrogen from renewable electrolysis, “blue” hydrogen with carbon capture and storage (CCS), “turquoise” hydrogen via methane pyrolysis, and “pink” hydrogen using nuclear electricity each present distinct advantages, challenges, and policy implications.

This workshop provides a comprehensive and comparative assessment of hydrogen production pathways, examining lifecycle emissions, cost structures, scalability, infrastructure compatibility, and regulatory classification frameworks. It explores the debate surrounding low-carbon versus renewable hydrogen, methane leakage considerations, carbon capture performance thresholds, and sustainability certification standards.

Academic speakers will provide lifecycle analysis (LCA), techno-economic comparisons, and system-level modelling. Industry experts will present real-world deployment cases across different hydrogen colours. Policy representatives will address taxonomy alignment, emissions thresholds, and international harmonisation of definitions.

The objective of this workshop is to provide clarity in a landscape often characterised by terminology confusion, enabling policymakers, investors, and industry leaders to make informed decisions grounded in scientific evidence and market realities.

This sub-workshop examines the critical interface between renewable electricity generation and green hydrogen production, a cornerstone for decarbonising energy-intensive sectors. As electrolysers increasingly operate under variable and intermittent power conditions, advanced system design, control strategies, and grid integration concepts are required to maintain efficiency, durability, and economic viability.

The session explores both direct coupling of electrolysers with wind and solar power plants and hybrid grid-connected configurations, with a focus on maximising utilisation while ensuring system reliability. Hydrogen systems can provide valuable flexibility services, helping to stabilise electricity grids with high shares of variable renewables.

Academic presentations will cover modelling approaches, optimisation techniques, and degradation effects on electrolyser performance, providing the scientific foundation for robust system design. Industry speakers will share practical insights and lessons learned from large-scale renewable hydrogen projects, including integration strategies, operational challenges, and real-world performance metrics.

The workshop is designed to provide actionable recommendations for engineers, project developers, policymakers, and investors involved in renewable hydrogen deployment.

Water electrolysis is the cornerstone technology for green hydrogen production, converting renewable electricity into a zero-carbon energy carrier. This sub-workshop explores both commercially mature and emerging electrolysis technologies, including Alkaline, Proton Exchange Membrane (PEM), Anion Exchange Membrane (AEM), and Solid Oxide Electrolysis (SOEC).

Key topics include efficiency, durability, materials innovation, and scalability for industrial deployment. Presentations will examine recent scientific breakthroughs, unresolved challenges, and pathways to gigawatt-scale manufacturing. Industry speakers will provide practical insights on production scale-up, supply-chain management, and commercial strategies, including cost reduction and market adoption.

The workshop is intended to provide a comprehensive understanding of electrolysis technology readiness, sectoral applicability, and integration into renewable energy systems, equipping participants with knowledge to guide R&D investment and industrial deployment.

This workshop examines catalyst development, membrane innovation, and the strategic challenge of critical raw materials such as iridium and platinum-group metals. It addresses substitution strategies, recycling technologies, material efficiency improvements, and circular design principles.

The session aligns with EU Raw Materials Strategy objectives and explores how material supply security influences hydrogen cost and scalability.

Water availability and sustainability are critical enablers for the large-scale deployment of green hydrogen. This sub-workshop explores water sourcing strategies, regional constraints, and environmental sustainability considerations across diverse geographies. It addresses the integration of desalination, use of alternative water sources, and assessment of environmental impacts throughout the hydrogen production life cycle.

Participants will gain insights into techno-economic and environmental trade-offs, including water footprint, lifecycle emissions, and resource efficiency. Academic contributions focus on modelling water demand, innovative desalination and recycling technologies, and lifecycle assessment (LCA) methodologies. Industry speakers present practical approaches to water management, environmental permitting, and sustainability reporting from operational projects in water-scarce regions.

The workshop aims to provide participants with actionable guidelines to ensure green hydrogen production is both environmentally responsible and operationally feasible, particularly in regions with limited freshwater resources.

Hydrogen storage is a critical component for enabling flexibility in the energy system, balancing supply and demand, and supporting seasonal and long-term energy storage. This sub-workshop explores a wide range of hydrogen storage solutions, including physical storage (compressed and liquid), solid-state storage (metal hydrides and chemical hydrides), and chemical carriers such as ammonia and liquid organic hydrogen carriers (LOHCs).

The workshop addresses performance, safety, scalability, and cost considerations, providing participants with a holistic understanding of storage options and their integration into hydrogen supply chains. Academic presentations will focus on storage materials, reaction kinetics, and system modelling, while industry speakers will share lessons from large-scale implementation, safety management, and infrastructure deployment.

The workshop aims to provide actionable guidance for technology selection, operational planning, and investment in hydrogen storage, supporting both energy and industrial applications.

The large-scale deployment of green hydrogen critically depends on the availability of safe, efficient, and cost-effective transport and distribution infrastructure. This sub-workshop focuses on the full spectrum of hydrogen transport pathways, including dedicated and repurposed pipelines, maritime transport of hydrogen and hydrogen derivatives, and regional distribution networks connecting production sites with end users.

The workshop addresses key technical challenges such as hydrogen embrittlement, material compatibility, compression and liquefaction requirements, and system efficiency losses along the transport chain. In parallel, it explores infrastructure planning, cross-border connectivity, and logistics optimisation under different market development scenarios.

Academic contributions will provide insight into materials science, infrastructure modelling, and system optimisation, while industry speakers will present real-world experiences from pipeline operators, shipping companies, and transmission system operators actively developing hydrogen corridors and international supply chains. The session aims to support strategic decision-making for hydrogen infrastructure investments at regional, national, and international levels.

Green hydrogen production depends on renewable electricity availability. This workshop explores dynamic electrolyzer operation, grid balancing, curtailment mitigation, hybrid renewable-hydrogen plants, offshore wind coupling, and hydrogen as seasonal storage.

It examines electricity market design, flexibility pricing, and sector coupling under EU electricity market reform proposals.

The decarbonisation of energy-intensive and hard-to-abate industrial sectors is one of the most critical challenges in the transition to a net-zero economy. Green hydrogen offers a versatile solution as both a low-carbon energy carrier and a sustainable feedstock for industrial processes. This sub-workshop focuses on the utilisation of green hydrogen in steel production, chemicals, refining, fertilisers, and other high-temperature or feedstock-intensive industries.

The workshop addresses key technical and operational challenges, including process integration, retrofitting of existing assets, hydrogen supply reliability, and operational flexibility. It also explores scale-up pathways from pilot and demonstration projects to full commercial deployment, considering impacts on process efficiency, product quality, and cost competitiveness.

Academic contributions will provide insights into process modelling, reaction kinetics, and system integration strategies, while industry speakers will share lessons learned from flagship industrial hydrogen projects. The session aims to identify realistic deployment pathways and prioritise actions needed to accelerate industrial adoption of green hydrogen.

Decarbonising the transport sector requires a portfolio of solutions tailored to different use cases, operating profiles, and infrastructure constraints. Green hydrogen plays a particularly important role in transport segments where high energy density, fast refuelling, and long operational range are essential. This sub-workshop explores hydrogen applications across heavy-duty road transport, rail, maritime shipping, and aviation, with a focus on technical feasibility, infrastructure requirements, and market uptake.

The workshop examines fuel cell system performance, vehicle integration challenges, refuelling infrastructure design, and the interplay between hydrogen mobility and alternative low-carbon solutions such as battery-electric systems and synthetic fuels. Academic contributions provide system-level analysis and comparative assessments, while industry speakers share insights from vehicle manufacturing, infrastructure deployment, and fleet operation. The session aims to identify transport segments where hydrogen offers the strongest value proposition and to define realistic rollout pathways.

Power-to-X (PtX) technologies convert renewable electricity into hydrogen and hydrogen-derived energy carriers, enabling integration across multiple sectors including fuels, chemicals, and energy storage. This sub-workshop explores the full spectrum of PtX pathways, including Power-to-Ammonia, Power-to-Methanol, Power-to-Fuels, and hydrogen-based synthetic chemicals.

The workshop addresses technical, economic, and system-level challenges such as conversion efficiency, integration with renewable power, seasonal energy storage, and cross-sector market dynamics. Academic contributions focus on process design, system modelling, and optimisation, while industry speakers share experiences from pilot projects, industrial demonstrations, and large-scale deployment initiatives.

Participants will gain insights into how PtX can support decarbonisation goals, reduce fossil fuel dependence, and create new hydrogen value chains. The session also evaluates market potential, cost trajectories, and scalability to identify priority pathways for policy and investment.

Safety, standardisation, and certification are critical enablers for the widespread adoption of hydrogen technologies. Without harmonised safety frameworks and internationally recognised certification schemes, industrial deployment, cross-border trade, and public acceptance could face significant barriers.

This sub-workshop focuses on hydrogen safety science, risk assessment methodologies, codes and standards development, and certification mechanisms including Guarantees of Origin. It also addresses practical challenges such as emergency response planning, operational risk management, and safety compliance across industrial, mobility, and storage applications.

Academic presentations will cover the latest research on hydrogen risk assessment, material and system safety, and standardisation strategies, while industry speakers will share real-world experience implementing safety management systems, certifying projects, and gaining public trust. The workshop aims to produce actionable recommendations for policymakers, regulators, and industrial stakeholders to ensure safe, reliable, and trusted hydrogen deployment at scale.

The commercial deployment of green hydrogen requires a deep understanding of techno-economic drivers, policy frameworks, and investment mechanisms. This sub-workshop addresses the financial, regulatory, and market conditions necessary to scale hydrogen production, distribution, and utilisation while achieving cost competitiveness with fossil-based alternatives.

Topics include cost modelling, learning curves, investment risk management, market design, and policy instruments such as subsidies, carbon pricing, and Guarantees of Origin. Academic presentations will provide rigorous economic analysis, cost forecasting, and market scenario modelling. Industry speakers will share experiences in project financing, risk mitigation, and establishing bankable business models for hydrogen projects.

The workshop aims to equip participants with practical tools to evaluate the economic feasibility of hydrogen projects, identify policy gaps, and design market mechanisms that support large-scale adoption.

The large-scale deployment of green hydrogen depends not only on technological progress but fundamentally on the establishment of transparent, predictable, and bankable pricing mechanisms. Unlike conventional commodities, hydrogen markets are still emerging, with pricing frameworks evolving across regions, sectors, and regulatory environments. This workshop addresses the economic architecture required to establish robust hydrogen price formation, revenue certainty, and long-term market stability.

Key themes include levelised cost of hydrogen (LCOH), price discovery mechanisms, Contracts for Difference (CfDs), hydrogen auctions, carbon pricing interactions, Guarantees of Origin (GoO), and cross-border trade benchmarks. The workshop will explore how hydrogen prices can be structured to reflect production costs, system value, carbon intensity, and security-of-supply considerations.

Academic speakers will provide analytical perspectives on pricing theory, cost structures, and market design. Industry and policy experts will share insights on auction mechanisms, long-term offtake agreements, risk-sharing structures, and the integration of hydrogen into electricity and carbon markets.

The workshop aims to equip participants with a comprehensive understanding of hydrogen price formation, practical tools for structuring bankable revenue models, and policy recommendations to ensure stable and competitive hydrogen markets in Europe and globally.

Green hydrogen reshapes global energy trade patterns. This workshop examines EU import strategies from North Africa, the Middle East, Australia, and Latin America. It addresses infrastructure diplomacy, supply security, trade agreements, and geopolitical risk.

Energy sovereignty and strategic autonomy considerations are integrated into the discussion.

This forward-looking workshop evaluates next-generation hydrogen technologies including high-temperature electrolysis, artificial photosynthesis, advanced synthetic fuels, digital twins, AI-based optimisation, and water resource constraints.

It assesses long-term sustainability limits and system resilience under climate stress scenarios.

Machine learning has entered the vocabulary of every branch of physical science, yet its relationship to es- tablished practices of statistical modelling and scientific inference is often poorly articulated. This opening workshop addresses that gap directly. It establishes what machine learning is and is not, situating the field within the broader landscape of data analysis that physical scientists already practise, and drawing a careful distinction between fitting a model to data and learning generalisable structure from it.

The workshop introduces core concepts — loss functions, regularisation, overfitting, cross-validation, and the bias–variance trade-off — through examples drawn from spectroscopy, thermodynamics, and materials prop- erty prediction rather than from image classification or commercial recommender systems. Supervised learning (regression and classification), unsupervised learning (clustering, dimensionality reduction, latent-variable mod- els), and probabilistic framing (likelihood, Bayes’ theorem, posterior predictive distributions) are developed with attention to the kinds of data, noise models, and physical constraints that characterise experimental and computational science.

The session also addresses model selection and validation methodology — train/validation/test splits, k-fold cross-validation, and learning curves — emphasising the pitfalls that arise when small, expensive datasets are involved, as is typical in physical science. Participants will gain a clear conceptual foundation for the methods developed in subsequent workshops, together with practical guidance on when ML adds genuine value to a scientific investigation and when simpler or more interpretable approaches are preferable.

Neural networks underpin the majority of recent breakthroughs in scientific machine learning, yet their inner workings are often treated as opaque. This workshop provides a conceptual and practical introduction to feedforward neural networks, moving from the single perceptron to deep architectures, with emphasis on building physical intuition for what a neural network is doing at each layer — function composition, representation learning, and non-linear basis expansion.

The session develops backpropagation and automatic differentiation as computational tools, and examines the practical choices that determine network performance: activation functions, batch normalisation, dropout, and learning rate schedules. Convolutional neural networks for structured data and recurrent architectures are surveyed briefly to establish vocabulary. Critically, the workshop introduces the concept of symmetry and equivariance — the principle that physical systems possess rotational, translational, and permutational symmetries that generic architectures do not respect — setting the stage for the equivariant architectures developed in depth in Workshops 5, 6, and 7.

Participants will leave with a working understanding of how neural networks learn representations from data, how architectural choices encode assumptions about the problem structure, and why physical systems demand specialised network designs that generic deep learning does not provide.

The bridge between raw physical data and a machine-learnable format is often the most consequential design decision in the entire ML workflow. A poorly chosen representation can render even the most powerful model ineffective, while a well-designed descriptor can make a simple model competitive with far more complex alter- natives. This workshop examines how molecules, crystals, spectra, voltammograms, and simulation trajectories are encoded as inputs to ML models, and develops the principles — invariance, locality, and smoothness — that make a representation physically well-behaved.

The session covers hand-crafted molecular descriptors (SMILES strings, fingerprints, Coulomb matrices, SOAP and other invariant descriptors), crystal structure encodings (space groups, Wyckoff positions), and represen- tations for electrochemical data including feature extraction from cyclic voltammetry, impedance spectra, and galvanostatic cycling curves. The treatment of learned representations complements the discussion of hand- crafted descriptors, and the workshop addresses practical challenges common in experimental physical science: normalisation, imputation, handling of small datasets, and data augmentation strategies for physical data.

Participants will develop a systematic framework for evaluating and selecting representations, understanding the trade-offs between interpretability and expressiveness, and recognising how descriptor choice interacts with model architecture and the physical symmetries of the system under study.

Gaussian processes occupy a distinctive position in scientific machine learning: they make predictions with calibrated uncertainties, incorporate prior physical knowledge through kernel design, and degrade gracefully when data are scarce — properties of particular value in the physical sciences where measurements are expensive and datasets are small. This workshop develops Gaussian process regression from first principles, covering kernel functions, hyperparameter optimisation, and predictive distributions, with emphasis on what uncertainty quantification means in practice and why scientists should care about it.

The session then examines Bayesian optimisation — the methodology that uses GP-based surrogate models to guide sequential experimental or computational searches toward optimal conditions with minimal evaluations. Acquisition functions (expected improvement, upper confidence bound, entropy search) and the exploration– exploitation trade-off are developed with examples from materials science and electrochemistry. The closely related methodology of active learning, in which uncertainty estimates are used to decide which experiments or calculations to prioritise next, is examined in the context of both high-throughput computation and laboratory experimentation.

The workshop also addresses the GAP/SOAP framework, in which Gaussian process regression underpins the construction of interatomic potentials with systematically improvable accuracy, providing a bridge to the ML force field methods covered in Workshop 6.

Molecules and crystals are naturally graphs: atoms are nodes, and bonds or spatial proximity define edges. This structural correspondence makes graph neural networks the architecture of choice for learning structure–property relationships across chemistry and materials science. This workshop traces the development of GNNs from the foundational Message Passing Neural Network framework through to crystal graph convolutions, directed message passing, and modern equivariant architectures that encode rotational and translational symmetry directly into the network.

The session examines Crystal Graph Convolutional Neural Networks (CGCNN) and MEGNet for solid-state property prediction, directed message passing networks (Chemprop) for molecular property and reaction predic- tion, and the extension to equivariant GNNs that respect the geometric symmetries of three-dimensional atomic arrangements. Applications span molecular property prediction, crystal stability and band gap estimation, re- action outcome forecasting, and materials design. Benchmark datasets — QM9 for small organic molecules, the Materials Project for inorganic crystals, and emerging electrochemical datasets — are discussed both as practical resources and as illustrations of the strengths and limitations of current evaluation methodology.

Participants will gain a comprehensive understanding of how graph-based architectures encode chemical struc- ture, the role of symmetry in determining model accuracy and data efficiency, and the current capabilities and limitations of GNN-based property prediction.

One of the most transformative applications of machine learning in the physical sciences has been the creation of interatomic potentials that replicate the accuracy of quantum chemical calculations — density functional theory, coupled cluster, or other electronic structure methods — at a fraction of the computational cost. These ML potentials enable molecular dynamics and Monte Carlo simulations of thousands of atoms over nanosecond timescales, accessing regimes of system size and simulation length that were previously intractable without sacrificing chemical accuracy.

This workshop traces the development of ML interatomic potentials from the foundational Behler–Parrinello neural network potentials and their atom-centred symmetry functions, through the ANI family of transferable potentials for organic chemistry and the SchNet architecture with continuous-filter convolutions, to the current state of the art in equivariant architectures — NequIP and MACE — which achieve remarkable data efficiency by encoding the symmetries of three-dimensional space directly into the network architecture.

The session addresses the full workflow of ML potential development: training data generation from electronic structure calculations, active learning strategies for expanding training sets efficiently, uncertainty-aware molec- ular dynamics (FLARE), and validation against experimental and computational benchmarks including radial distribution functions, phonon spectra, melting points, and thermodynamic properties. Participants will gain a thorough understanding of the accuracy–speed trade-off landscape, the practical steps involved in constructing and validating an ML potential for a system of interest, and the current capabilities and limitations of these methods for both molecular and condensed-phase systems.

Standard data-driven models can learn physically implausible relationships, extrapolate poorly beyond their training domain, and require large datasets to achieve acceptable accuracy. Physics-informed machine learning addresses all three limitations by embedding known governing equations, conservation laws, and symmetry constraints directly into the learning framework — either through the loss function, the network architecture, or both.

This workshop surveys the landscape of physics-informed approaches, beginning with physics-informed neural networks (PINNs), which incorporate the residuals of partial differential equations as additional loss terms via automatic differentiation. It then examines operator learning methods — DeepONet and neural operators — that learn mappings between function spaces and enable rapid solution of parametric PDEs. The treatment of symmetry as inductive bias, developed through the Erlangen Programme perspective of geometric deep learning, connects to the equivariant architectures encountered in Workshops 5 and 6. Symbolic regression methods (SINDy, PySR), which aim to recover closed-form analytic equations from data, are presented as a complementary route to interpretable physical models.

Examples are drawn from fluid mechanics, quantum mechanics, reaction-diffusion systems, and electrochemical transport, with particular attention to the combination of physics constraints with experimental electrochemical data. The workshop also addresses the limitations of physics-informed approaches, examining where they succeed and where they struggle, to support realistic expectations and informed method selection.

Beyond prediction, machine learning can propose entirely new molecules and crystal structures with desired properties, inverting the structure–property relationship to enable de novo design. This workshop explores the full spectrum of generative approaches applied to chemical and materials space, including variational autoen- coders (VAEs), generative adversarial networks (GANs), diffusion models, and flow-based methods.

The session examines how latent space representations of chemical structure are constructed, focusing on the Junction Tree VAE for molecular graphs and the Crystal Diffusion Variational Autoencoder (CDVAE) for periodic crystal structures. Conditioning strategies that enable the generation of structures with specified target properties are developed alongside the critical question of validation: how to assess whether a generated structure is thermodynamically stable, synthesisable, and genuinely novel. The landmark GNoME result from DeepMind — the prediction of 2.2 million stable inorganic crystal structures — illustrates both the ambition and the current state of the field, and the workshop examines what experimental follow-through and validation actually require to move from predicted structures to synthesised compounds.

Participants will gain a clear understanding of the capabilities and practical limitations of current generative approaches, the validation requirements for scientific credibility, and the prospects for generative design as a component of the materials discovery pipeline.

Electrochemical experiments produce rich, high-dimensional datasets — impedance spectra, cyclic voltammo- grams, galvanostatic cycling profiles, chronoamperometric transients — that are difficult to interpret fully by conventional analytical methods. Machine learning offers new routes to automated feature extraction, degra- dation diagnostics, state-of-health estimation, and closed-loop protocol optimisation, with the potential to accelerate both fundamental understanding and practical deployment of electrochemical technologies.

This workshop examines the state of the art in ML-assisted electrochemical analysis. It addresses the landmark studies on data-driven prediction of battery cycle life from early-cycle data, Gaussian process-based analysis of electrochemical impedance spectroscopy for degradation mode identification, and Bayesian closed-loop optimi- sation of fast-charging protocols — studies that demonstrate the power of ML to extract information that is present in electrochemical data but inaccessible to conventional analysis.

The session also explores ML applications for molten salt systems — conductivity, viscosity, and phase behaviour prediction — and discusses the challenges of open datasets, reproducibility, and standardised benchmarks in electrochemical ML. Participants will develop practical knowledge of how to apply ML methods to their own electrochemical data, an understanding of the validation requirements for credible results, and an appreciation of the opportunities for ML to transform electrochemical science and engineering.

High-throughput computation and experiment now generate enormous datasets that machine learning can mine for structure–property patterns and use to guide the search for new materials with targeted functionalities. This workshop examines the full discovery pipeline, from dataset construction and surrogate model training through uncertainty-guided search to experimental validation, addressing the practical realities of deploying ML in a materials discovery programme.

The session covers the major open materials databases — the Materials Project, AFLOW, OQMD — and examines how surrogate models for formation energy, band gap, ionic conductivity, and transport properties are constructed, validated, and deployed. The comparative merits of high-throughput DFT screening and ML- guided search are assessed, together with multi-objective optimisation strategies for materials with competing property requirements. Case studies span new stable inorganic compounds (the GNoME campaign), solid electrolytes for battery applications, and photovoltaic absorber materials.

A critical theme throughout the workshop is the gap between computational prediction and experimental realisation — the “validation bottleneck” — and the strategies being developed to bridge it. Participants will leave with a comprehensive understanding of the ML-accelerated discovery pipeline, the infrastructure and data resources available, and the practical steps required to move from predicted candidates to synthesised and characterised materials.

Vibrational spectroscopy (infrared, Raman), X-ray diffraction, nuclear magnetic resonance, and electron mi- croscopy are the primary experimental windows through which materials scientists and chemists observe struc- ture, bonding, and composition. Machine learning is transforming each of these characterisation techniques: automating phase identification, deconvoluting overlapping signals, predicting spectra from first-principles cal- culations, and enabling the solution of inverse problems — inferring structure from spectral data.

This workshop examines these developments with worked examples drawn from inorganic chemistry and mate- rials characterisation. It addresses convolutional neural networks for automated phase identification from X-ray diffraction patterns, ML-assisted deconvolution of complex vibrational spectra, graph neural network and ML potential approaches to predicting NMR chemical shifts and IR/Raman spectra, and deep learning methods for electron microscopy image analysis including defect detection and atomic structure reconstruction.

The session also addresses the practical challenge of transfer learning — adapting models trained on simulated spectra to experimental data with different noise characteristics, resolution, and baseline artefacts — a problem of particular importance given that training data are often generated computationally while deployment targets experimental measurements. Participants will gain practical guidance on applying ML to their own charac- terisation data and a clear understanding of where these methods are mature and where significant challenges remain.

Large language models are rapidly becoming embedded in the scientific workflow, offering capabilities in lit- erature review, writing assistance, code generation, data extraction from published papers, and hypothesis formulation. This workshop provides a critical and practical treatment of these tools, addressing what LLMs actually do, where they are reliable, where they hallucinate, and how researchers can use them effectively without compromising scientific rigour.

The session develops the technical foundations — tokenisation, attention mechanisms, in-context learning, and fine-tuning — at a level sufficient for researchers to make informed judgements about model capabilities and limitations. It then examines practical deployment: prompt engineering for scientific tasks, retrieval-augmented generation (RAG) for literature-grounded answers, and automated information extraction from scientific papers. Specialised scientific language models, including ChemBERTa, MatSciBERT, and Galactica, are assessed against general-purpose LLMs on scientific reasoning benchmarks.

The workshop gives particular attention to hallucination, provenance, and reproducibility concerns that are especially acute in academic contexts. AI-assisted code generation for scientific computing is addressed as a practical tool, with guidance on verification and testing practices. Participants will leave equipped to adopt LLMs and related AI tools thoughtfully and productively, understanding both their power and their limitations in a research setting.

The convergence of Bayesian optimisation, robotic automation, and real-time machine learning analysis has produced a new class of experimental platform: the self-driving laboratory, which closes the loop between measurement and experimental design without human intervention. These systems promise to accelerate dis- covery by orders of magnitude, but their effective deployment requires careful integration of domain knowledge, uncertainty quantification, and experimental infrastructure.

This workshop examines the conceptual and practical foundations of autonomous experimentation, developing the design–measure–learn–decide cycle that underpins all closed-loop systems. Bayesian optimisation, intro- duced in Workshop 4, is revisited here as the decision engine that selects the next experiment based on surro- gate model predictions and uncertainty estimates. Multi-fidelity strategies that combine cheap computational models with expensive experimental measurements are examined as a route to efficient resource allocation.

The session reviews notable implementations in chemistry and materials science, including photocatalyst dis- covery campaigns, flow chemistry optimisation, and the self-driving laboratory platforms developed by the Aspuru-Guzik group and collaborators. The landmark closed-loop optimisation of battery fast-charging proto- cols provides an electrochemical case study of particular relevance. The workshop also addresses the practical challenges of infrastructure integration, data standards, reproducibility, and the respective roles of human-in- the-loop and fully autonomous approaches.

A prediction is scientifically useful only when it can be understood, interrogated, and trusted. In the phys- ical sciences, the goal of explanation extends beyond verifying that a model performs well on held-out data: researchers seek to extract governing mechanisms, identify the physical features that drive predictions, and determine whether a model has learned physically meaningful relationships or merely exploited statistical cor- relations in the training set.

This workshop surveys the landscape of explainability methods, distinguishing between inherently interpretable models and post-hoc explanation of opaque architectures. It examines SHAP values and their use for feature importance analysis in molecular and materials models, attention mechanisms and what they do (and do not) reveal about learned representations, gradient-based saliency maps for spectral and image data, and symbolic regression as a route to recovering closed-form physical laws from trained models.

The session develops scientific standards for explanation, addressing the question of when an explanation is physically meaningful as opposed to merely statistically informative. Participants will gain practical tools for interrogating their own models and a critical framework for evaluating explainability claims in the literature

The reproducibility crisis is not unique to machine learning, but ML introduces specific new failure modes that the physical sciences must confront: data leakage between training and test sets, benchmark overfitting through repeated evaluation on the same held-out data, undisclosed preprocessing and hyperparameter tuning choices, and the conflation of held-out test performance with real-world generalisation capability. These problems are amplified in the physical sciences, where datasets are often small, measurements are expensive, and the gap between published benchmarks and laboratory deployment can be substantial.

This workshop develops good practice for the full scientific ML lifecycle, covering data curation, splitting strategies appropriate for chemical and materials data (where naive random splits can introduce systematic leakage), hyperparameter tuning protocols, and reporting standards. It examines benchmark design and its pathologies, using the QM9 molecular dataset and the Materials Project as case studies that illustrate both the value and the limitations of community benchmarks.

The session also addresses practical infrastructure for reproducible research: version control (Git), data ver- sioning (DVC), experiment tracking (MLflow, Weights & Biases), and community platforms for sharing models, data, and code (Hugging Face, Zenodo, Materials Cloud). Participants will leave with a concrete workflow for conducting and reporting ML research to the standards expected by the physical science community.

The final workshop broadens the lens to the ethical and societal dimensions of deploying AI and machine learning in the physical sciences. While ethical AI is often discussed in the context of facial recognition, hiring algorithms, or social media, the physical sciences present their own distinctive challenges: dual-use risks in AI- accelerated materials discovery and synthesis planning, the environmental costs of large-scale model training, attribution and credit in human–AI collaborative research, and the risk of perpetuating biases embedded in historical datasets — for example, the overrepresentation of certain element combinations or synthesis routes in public materials databases.

The session addresses bias in training data, examining what enters public materials databases, what is excluded, and how these selection effects propagate into model predictions and discovery recommendations. Dual-use risks are considered in the context of ML-guided synthesis planning and materials design, where the same tools that accelerate beneficial discovery can in principle be directed toward harmful applications. The environmental cost of training large ML models is quantified and contextualised. The workshop also examines the changing nature of scientific authorship and peer review in the age of AI-generated content, addressing questions of attribution, transparency, and the integrity of the scientific literature.

Relevant regulatory frameworks, including the EU AI Act and its implications for research institutions, are discussed alongside principles for responsible deployment: uncertainty disclosure, human oversight, and re- versibility. Participants will gain a structured framework for identifying and managing the ethical dimensions of ML deployment in their own research programmes.