Dunia Innovations unveils €280M Berlin GigaLab to industrialise AI-driven materials discovery

2026-05-20 · Source: Tech.eu - Tech.eu · Field: Science & Research — Artificial Intelligence & Machine Learning, Engineering & Applied Sciences, Research Methodology & Innovation · Depth: Expert, quick

Summary

Dunia Innovations has announced the Berlin GigaLab, a €280 million, 6,000 m² autonomous R&D facility set to industrialize AI-driven materials discovery. Backed by Siemens, ABB Robotics, NVIDIA, and AWS, the GigaLab aims to establish Europe as a global hub for next-generation deeptech materials innovation. Founded in 2022, Dunia's platform integrates AI, lab automation, and simulation into a closed-loop system, with its second-generation IRIS platform launched in May 2025. The GigaLab will integrate autonomous experimentation, AI-guided design, digital simulation, and industrial-grade characterisation to serve sectors like energy storage, catalysis, and semiconductors. This facility, expected to create over 200 direct jobs and begin operations in 2028, addresses the bottleneck of experimental verification for millions of AI-proposed material candidates. An industrial consortium including Siemens, ABB Robotics, AWS, NVIDIA, ILS, and Merck supports the project, emphasizing its strategic relevance for European competitiveness.

Key takeaway

For Directors of AI/ML or Research Scientists focused on advanced materials, Dunia Innovations' Berlin GigaLab signals a critical shift towards industrial-scale experimental verification. You should re-evaluate your R&D infrastructure investments, recognizing that AI's ability to propose millions of new materials now demands purpose-built facilities for high-throughput validation. Consider exploring partnerships or adopting integrated AI, automation, and simulation platforms to generate the structured, industrially relevant data essential for accelerating next-generation material development and maintaining competitive advantage.

Key insights

The bottleneck in AI-driven materials discovery has shifted from candidate generation to industrial-scale experimental verification.

Principles

• Industrial-scale science requires purpose-built infrastructure.
• Integrated AI, automation, and workflow design are crucial.
• Fragmented scientific data hinders large-scale AI model training.

Method

GigaLab integrates autonomous experimentation, AI-guided design, digital simulation, and industrial-grade characterisation into a single closed-loop platform for materials discovery and validation.

In practice

• Generate structured, multimodal, industrially relevant data.
• Apply AI, lab automation, and simulation in closed-loop systems.
• Address material bottlenecks in frontier technologies.

Topics

• AI-driven Materials Discovery
• Lab Automation
• Industrial R&D Infrastructure
• Deeptech Innovation
• Energy Storage Materials
• Semiconductor Materials

Best for: Investor, AI Scientist, Research Scientist, Director of AI/ML

Related on AIssential

• AI materials discovery now needs to move into the real world — AI-driven autonomous labs aim to revolutionize materials discovery by accelerating synthesis and testing, despite current limitations.
• AI for Atoms: How Periodic Labs is Revolutionizing Materials Engineering with Co-Founder Liam Fedus — AI's next frontier involves connecting intelligent systems to the physical world for scientific and material discovery.
• From Materials Database to Materials Bank: Assetizing Data for AI Driven Materials Innovation — The Materials Bank assetizes filtered materials data into standardized assets, bridging discovery and industrial application.
• 🔬 The Self-Driving Lab — Joseph Krause, Radical AI — Self-driving labs, integrating AI and automation, accelerate complex materials discovery by overcoming human bias and capturing comprehensive experimental data.
• Bridging AI & Science: The Impact of Machine Learning on Material Innovation with Joe Spisak of Meta — AI, especially machine learning, is fundamentally reshaping material innovation and scientific discovery.
• AI breakthrough could replace rare earth magnets in electric vehicles — AI can dramatically accelerate the discovery of new high-temperature magnetic materials, reducing rare-earth dependence.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Tech.eu - Tech.eu.