DiscoveryLabNL
Autonomous Closed-Loop Experimentation for Materials Discovery
The Challenge
Artificial intelligence and high-throughput laboratory automation have the potential to fundamentally transform materials and molecular research, accelerating the optimisation of desired properties, uncovering unique parameter sets, and enabling the design of materials with unprecedented features.
This potential is widely recognised across the broader materials community, yet has also produced fragmentation: parallel, disconnected efforts that fall short of the systemic integration needed to realise AI's full scientific impact.
DiscoveryLabNL is TU/e's answer: a shared infrastructure for autonomous materials discovery, integrating self-driving laboratories, advanced multimodal characterisation, and an AI Core to accelerate the design, synthesis, and validation of next-generation materials.
TU/e's ICMS has built nationally recognised SDL foundations through the NWO Gravitation Programme Interactive Polymer Materials (IPM) and the National Growth Fund programme Big Chemistry, establishing a proven platform for this next step.
The stakes extend well beyond the laboratory. The energy transition, the shift toward circular material flows, and the search for sustainable alternatives to critical raw materials are all bottlenecked by the pace of materials discovery. These are not problems that incremental experimentation can solve at the speed society requires. Infrastructure that systematically accelerates the design, synthesis, and validation of new materials is no longer a competitive advantage; it is a societal necessity.
Three Pillars
Self-Driving Laboratories
Robotic synthesis and processing platforms, automated sample handling, in-line sensing and feedback systems. AI-guided closed-loop experimentation across a hub-and-spoke network of SDL nodes.
Advanced Characterisation
Optical, electron and X-ray microscopy combined with spectroscopic techniques. Serves as the continuous, real-time verification and integration layer of the autonomous discovery loop.
AI Core
FAIR data backbone, curated model catalogue, dedicated AI science team — the experimental data engine and infrastructure for the next generation of materials foundation models. Federated design serving the full consortium and national partners.
Discovery Domains
Priority research domains where autonomous, closed-loop experimentation can deliver transformative impact in advanced materials, energy, circularity, and soft matter.
Soft Matter and Biomaterials
Responsive hydrogels, self-assembling systems, and biocompatible materials for diagnostics, implants, and drug delivery.
Sustainable Energy
Accelerated design of electrolytes, electrode architectures, photocatalysts, and solar fuel systems for the energy transition.
Circular Materials
AI-guided discovery of recyclable, degradable, and bio-based polymers, composites, and inorganic systems to close the materials loop.
Molecular and Supramolecular Systems
High-throughput exploration of molecular libraries to accelerate discovery in coatings, optoelectronics, and precision functional materials.
Synthetic Biology Exploratory
Programmable living systems for on-demand biosynthesis of functional materials and therapeutic molecules.
Campus Consortium
Four partner institutes cover the full spectrum of materials research as equal governance partners. DIFFER is an NWO institute; ICMS, EIRES, and Casimir are TU/e institutes. EAISI (Eindhoven AI Systems Institute) provides a complementary talent pool and methodological foundation for the AI Core pillar.
ICMS
Institute for Complex Molecular Systems. Soft Matter, Supramolecular Chemistry, Synthetic Biology.
EIRES
Eindhoven Institute for Renewable Energy Systems. Energy Materials, Electrochemistry, Battery Technology.
DIFFER
Dutch Institute for Fundamental Energy Research (NWO). Plasma Physics, Fusion Materials, Energy Conversion.
Casimir
Casimir Research Institute. Semiconductors, Photonics, Quantum Technology.
The Global Landscape
Smart microscopy, autonomous instruments, and AI-driven spectroscopy are advancing rapidly worldwide — but groups pursue these single techniques in isolation. No major initiative integrates them into a multimodal closed loop. That requires cross-domain coordination no single group can achieve alone.
Acceleration Consortium
50 robots, 7 SDLs. World’s largest SDL programme. No characterisation pillar.
Korea 500 SDL
500 labs by 2030. National scale. XRD only, no multimodal characterisation.
DIADEM
Synchrotron + EM + spectroscopy. Advanced but not yet integrated into SDL loops.
CAPeX
Synchrotron PDF in SDL loop. Battery-only, single-technique per workflow.
Big Chemistry
Inline techniques (tensiometry, confocal microscopy, nanoindenter). Chemistry-specific, not cross-domain.
DiscoveryLabNL
Correlated multimodal characterisation. Cross-domain. Permanent research infrastructure.
For a global SDL landscape scan, explore the interactive SDL map.
Positioning
TU/e is uniquely positioned to lead this initiative by combining established self-driving lab capacity at ICMS, strong campus-wide materials expertise, and direct connections to national characterisation and digital infrastructure networks.
Within TU/e
Available for materials research across all TU/e departments, serving as foundational infrastructure for the 'Intelligent Materials Labs' 10-year vision of the TU/e Flagship: Advanced Materials.
Across the Netherlands
Open-access platform serving the wider Dutch materials research community, supporting national research sovereignty and EU materials autonomy objectives. National consortium formation underway with leading universities and NWO research institutes, as required for LSRI national infrastructure designation.
TU/e Investment
€20-30M indicative range. Subject to TU/e EB negotiation. Target cycle: NWO LSRI 2027.
Roadmap
Indicative timeline, subject to revision
Team
Core Leadership
Flagship Contributing Partners
Stakeholder Momentum
Strong institutional anchors, real momentum, active engagement. The concentric rings below show stakeholder commitment levels across the national materials discovery ecosystem. Click any name for details.
3 core · 19 committed · 5 in dialogue · 4 planned
We'd Love to Hear from You
This briefing is shared with TU/e internal stakeholders. We welcome colleagues who wish to contribute to shaping DiscoveryLabNL, whether scientifically or organisationally. Members of the LSRI Groups Technology, Materials, and Life Sciences & Enabling Technology are particularly encouraged to reach out so we can explore alignment and collaboration opportunities together.
General enquiries: discoverylabnl@tue.nl
Initiative Lead: Yuyang Wang ✉ email
Scientific Lead: Jan van Hest ✉ email
Organisation: ICMS · TU/e