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Mechanism-aware catalyst discovery
Catalysts and Reaction Materials
MatterLens helps teams reason across catalyst literature, reaction constraints, structure-property evidence, characterization data, and process targets. The platform is designed to surface testable active-site and formulation hypotheses, rank candidates, and close the loop as partner experiments return new performance data.
Connect catalyst composition, support, synthesis route, reaction condition, and characterization evidence.
Prioritize candidate formulations with explicit rationale and uncertainty.
Use active learning to choose the next synthesis, screen, or characterization run.
Problem context
Why this problem matters
Mechanistic ambiguity
Catalytic performance depends on active sites, supports, defects, promoters, poisons, and operating windows that are rarely captured in one clean dataset.
Sparse experiments
Each synthesis and screen is expensive, so discovery programs need evidence-weighted choices rather than broad enumeration.
Scale constraints
Promising catalysts must also make sense under feedstock, stability, regeneration, cost, and integration constraints.