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Dunia.ai · Project

AI-Driven Robotic Lab for Rapid Discovery of Carbon-Neutral Fuel Catalysts

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Imagine trying to find a needle in a haystack by hand; that is how discovering new chemical catalysts usually works. This project builds a smart robot that does the searching, testing, and learning automatically. It is like having a high-speed digital chef that experiments with thousands of recipes until it finds the perfect one for turning CO2 into fuel.

By the numbers
90%
reduction in discovery timelines
60-70%
reduction in R&D costs
20 years
typical discovery time for new catalysts
The business problem

What needed solving

Catalyst discovery is too slow and expensive, typically taking 20 years and millions of euros, which delays the transition to carbon-neutral industrial processes.

The solution

What was built

The IRIS platform, an autonomous lab combining robotic synthesis, electrochemical testing, and physics-informed AI models in a closed-loop system.

Audience

Who needs this

Green hydrogen producersCO2-to-fuel chemical plantsIndustrial catalyst manufacturersDecarbonization consultants
Business applications

Who can put this to work

Chemical Manufacturing
enterprise
Target: Industrial chemical producer

If you are an industrial chemical producer dealing with the slow pace of R&D for carbon-neutral processes — this project developed IRIS, an autonomous lab that reduces discovery timelines by up to 90%. This allows you to find high-performance catalysts for value-added chemicals much faster.

Renewable Energy
SME
Target: Green fuel startup

If you are a green fuel startup dealing with multi-million-euro R&D expenses — this project developed a closed-loop AI system that cuts R&D costs by 60-70%. This makes the development of carbon-neutral fuels financially viable.

Carbon Capture
mid-size
Target: CCU (Carbon Capture and Utilization) plant

If you are a CCU plant dealing with inefficient conversion of captured CO2 — this project developed a data-driven discovery platform that identifies catalysts capable of converting CO2 under realistic operating conditions.

Frequently asked

Quick answers

How does this impact R&D costs?

The technology cuts R&D costs by 60-70% compared to conventional catalyst discovery methods.

Can this be scaled to industrial levels?

Based on available project data, the system transforms catalyst R&D into a scalable, data-driven industrial process using robotics and AI.

What is the IP or licensing model?

Based on available project data, the project creates a strategic data and technology asset, but specific licensing terms are not disclosed.

How much faster is the discovery process?

The system reduces discovery timelines by up to 90% compared to traditional methods.

How is the system integrated into existing workflows?

It integrates laboratory automation, experiment orchestration, and machine-learning models into one end-to-end workflow.

Consortium

Who built it

The project is led by a single German SME, Dunia Innovations UG. With a 100% industry ratio and no university or research partners, the project is lean and focused on commercial application rather than academic exploration.

How to reach the team

Contact Dunia Innovations UG in Germany

Next steps

Talk to the team behind this work.

Contact us to explore partnerships for AI-driven catalyst discovery.