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A revolutionary approach to commercializing materials

Bridging the gap between discovery and deployment

Materials innovation is traditionally a linear and expensive process constrained by human ingenuity and reliant on slow iterations of design, build, and test. Mattiq upends this paradigm with massively parallel experimentation that mimics and miniaturizes real-world operating conditions, leveraging data and AI to accelerate the entire discovery process – which is now unbound by the limits of human intuition.

Transformative materials at unprecedented speed and scale


Proprietary nanoscale printing technology enables the creation of material megalibraries. Millions of materials are generated with controlled composition and size, and positionally encoded on a chip. By mimicking and miniaturizing real-world operating conditions and applying them to high-throughput screening techniques, we rapidly extract fundamental information from material candidates.
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Once new catalyst candidates are identified, they are scaled up via conventional synthesis techniques and integrated into laboratory-scale devices. Key metrics are then validated and optimized across activity, selectivity, durability, scalability, and availability to confirm the translation of properties from chip to device.
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The production of optimal materials are validated at industrial scale and, in collaboration with manufacturing partners, integrated into field-ready reactors. The real-world performance is mapped back to chip-level data, enabling us to refine and accelerate the end-to-end process for the next application.
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A new era of AI-driven materials science

AI has transformed numerous industries, from logistics to drug discovery, enabling faster design of better products. But, due to the low quality and quantity of available data, the AI revolution had not yet reached materials science — until now.
Through discovery, integration, and deployment, performance data continuously feeds into Mattiq’s historical knowledge base, building predictive power for which materials will perform well in industrial equipment – and which won’t. This generates massive datasets linking material composition to function across this entire development pipeline, accelerating the commercialization process for new applications and systems far beyond what is possible with traditional human-driven, linear innovation.
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