AI and the Green Deal
How Laboratories and Industry Are Shaping the Future Together
Agents, Material Intelligence and the Green Deal:
European industry is undergoing a historic transformation: The EU’s Green Deal pursues the ambitious goal of making Europe climate-neutral by 2050 — while safeguarding its economic competitiveness.
A key lever on this path is the development of sustainable materials. But how can innovation speed, resource efficiency, and regulatory requirements be harmonized? The answer lies in the intelligent handling of data — and a new generation of digital tools: AI agents based on Material Intelligence.
What is the European Green Deal?
The European Green Deal is the central climate protection and growth strategy of the European Union.
Its overarching goal: to make Europe the first climate-neutral continent by 2050. This means that no net greenhouse gases will be emitted — requiring profound changes across all sectors.
Specifically, the Green Deal includes:
• a reduction of greenhouse gas emissions by at least 55% by 2030 (compared to 1990 levels),
• investments in clean energy, sustainable industry, climate-friendly mobility, circular economy, and building efficiency,
• clear requirements for sustainable agriculture (“Farm to Fork”) and resource conservation,
• and the promotion of innovations that link climate goals and economic competitiveness.
Digitalization as an Enabler: Material Data at the Core
Industrial research and quality assurance are increasingly challenged to make faster, more informed, and forward-looking decisions. Instead of relying on lengthy test series, data is becoming the critical resource.
Many laboratories still work with scattered Excel sheets, incompatible standalone systems, or unstructured databases — leaving enormous potential untapped, both ecologically and economically.
However, many laboratories still work with scattered Excel sheets, incompatible standalone systems, or unstructured databases — leaving enormous potential untapped, both ecologically and economically. This is where the concept of Material Intelligence comes into play: the ability to collect, structure, and link relevant material data, and to turn it into actionable insights through AI-driven analyses.
Smart Assistance: AI Agents as Part of Material Intelligence
In addition to traditional databases and analysis tools, smart assistant systems are increasingly being deployed. AI agents can access existing material data, recognize patterns, make predictions, and develop targeted recommendations.
“AI agents work proactively toward achieving a goal — not just reactively answering commands,” explains Charles Jouanique, Chief Revenue Officer at LabV.
In a modern Material Intelligence platform like LabV, AI-powered modules, for example, analyze historical test results and suggest possible formulation improvements or alternative raw material combinations. “They help researchers and quality engineers make better decisions faster — without replacing human expertise,” adds Jouanique. This intelligent support becomes a decisive success factor, particularly in areas such as product development, recycling strategies, and material certification.
Practical Examples: Material Data for the Green Deal
As innovative approaches from industry and research show, structured material data is increasingly becoming the heart of sustainable developments. The following examples illustrate how digitalization and Material Intelligence are contributing to the practical implementation of the European Green Deal:
Example 1: Low-CO₂ Building Materials and Recycled Concrete
A project from Hesse demonstrates how material data can concretely help reduce CO₂ emissions. Companies like Heidelberg Materials are working on climate-friendly building materials, for instance by using recycled concrete and alternative binders. The goal is to significantly improve the CO₂ balance of buildings.
Here, structured material data is essential:
Raw material compositions, process parameters, and performance data are digitally recorded and analyzed to ensure both quality and sustainability. Digital platforms enable the optimization of formulations and transparent tracking of material flows. AI agents could additionally help identify the optimal mixture based on available recyclates and the requirements for strength, durability, and eco-balance.
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Example 2: Digital Product Passports for Battery Materials
Another pioneering project from Hesse focuses on the introduction of digital product passports for battery materials, supported by initiatives such as the Global Battery Alliance. The goal is to make information about raw materials, recyclability, and environmental impact digitally available across the entire lifecycle.
Material Intelligence plays a crucial role here: Data is not only stored, but also intelligently linked and analyzed to generate recommendations for second-life use, recycling strategies, or regulatory compliance.
Digital product passports enable transparent tracking and documentation along the entire value chain — an essential building block for a sustainable circular economy.
Example 3: Smart Coatings and Digital Twins
Applied research also increasingly combines material data and AI to develop more sustainable solutions. A compelling example comes from the field of smart coatings. At the Fraunhofer Institute for Material and Beam Technology IWS, researchers are developing intelligent protective coatings with embedded sensor functions — for instance, for self-healing, temperature monitoring, or early damage detection.
These new coatings are not only functional but also highly complex: even small changes in raw material parameters or the application process can impact their performance. To develop them efficiently and accurately, Material Intelligence platforms and digital twins are used. A digital twin — a data-based replica of all process, structural, and performance parameters — allows for the virtual testing of potential formulations before real experiments are conducted.
AI agents help identify parameter combinations with high success probabilities. This saves not only time but also raw materials and energy — two critical levers for achieving the Green Deal objectives. “Sustainability begins with the right data strategy — and ends with verifiable decisions,” aptly states the MaterialDigital research initiative of the German Federal Ministry of Education and Research (BMBF).
Example 4: More Efficient Coating Development with Material Intelligence
Industrial research also shows how material data can revolutionize processes. A client from the coatings industry tasked LabV with developing a coating solution for particularly challenging environmental conditions: high temperatures, specific adhesion requirements, and regulatory standards.
Instead of following the traditional trial-and-error approach, the laboratory relied on a Material Intelligence platform with an integrated AI agent. The agent analyzed historical formulations, test results, and environmental data, evaluated similarities, and developed proposals for optimized formulation combinations — including well-founded reasoning.
Why This Matters for the Green Deal
The position paper by Technologieland Hessen clearly states: Material technologies are not just technical solutions — they are drivers of transformation. Whether it’s CO₂-free chemistry, new energy storage materials, smart grids, or sustainable construction products — innovation arises where data, material knowledge, and digitalization converge.
“The transformation of industry can only succeed if digitalization and sustainability are thought of together — and material data is the common language,” emphasizes the initiative.
The European Green Deal not only demands technological innovations but also digital traceability — from raw material sourcing to recycling. Without centralized, structured material data, lifecycle analyses, digital product passports, or automated sustainability assessments are simply not feasible.
The technology is available — and the barriers to entry are lower today than ever before. The agent makes suggestions — the responsibility remains with the human.
Requirements for a Successful Start
The good news: The technology is available — and the barriers to entry are lower today than ever before.
What is needed:
• A centralized, structured material database (e.g., a platform like LabV),
• Clear goal definitions for the agent (“Find a suitable formulation under given conditions”),
• Feedback mechanisms for continuous improvement,
• And: the awareness that humans remain the final decision-makers.
“The agent makes suggestions — the responsibility remains with the human,” emphasizes Charles Jouanique.
A carefully selected pilot use case — such as in formulation development or recyclate evaluation — can already make the advantages tangible. It’s important not just to talk about AI — but to start applying it.
Conclusion: Those Who Act Now Will Shape the Transformation
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