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An experimental scientist is bounded by what fits in a lab — a few candidates per round, then iteration on the round-one winner. Our AI doesn't have that bound. It screens hundreds of thousands of materials, paired with the engineering systems they would operate in, in parallel. Not the same workflow accelerated. A different search — across the joint space of materials and systems. The fact that it finishes in weeks instead of years is a downstream consequence.
Industrial systems across energy, gas processing, and thermal management depend on the right materials — to store gases like hydrogen and methane, to capture or separate them as in CO₂ capture and biogas upgrading, to enable catalytic conversion, and to manage heat in data center cooling and long-duration energy storage. The candidates exist in principle: millions of porous frameworks with tunable properties for almost any application. The applications need them, urgently. What's missing is the bridge between them.
Traditional materials discovery is bottlenecked by experimental throughput — a handful of candidates per round, optimized against what fits in a lab. And materials are designed in isolation from the systems they will operate in, by different teams on different timelines. Two structural bottlenecks. Most of the achievable performance never gets built.
Planck builds that bridge. Our AI co-designs materials and the engineering systems they operate in — simultaneously, across hundreds of thousands of pairs. Top candidates move through experimental validation in our lab and at our research partners. We license validated material-system pairs to OEMs, EPCs, and industrial operators across energy, gas processing, and thermal applications. Materials discovery and system design stop being separate problems.














