Materials and Energy
Metal-Organic Frameworks
Generate novel MOF structures with target porosity, gas selectivity, and catalytic activity through systematic exploration of metal node, organic linker, and topology combinations.
The Challenge
Why Metal-Organic Frameworks needs a new approach to generation
The MOF design space is astronomically large — combinations of metal nodes, organic linkers, and framework topologies create millions of theoretically possible structures, yet only a tiny fraction have been synthesized and characterized. Current approaches rely on database screening of known or computationally enumerated MOFs, limiting discovery to variations on established reticular chemistry themes while novel topologies with potentially superior properties remain unexplored.
High-throughput computational screening evaluates gas adsorption properties for large databases of hypothetical MOFs but is limited to structures that can be enumerated from known building blocks and topologies. Machine learning models predict adsorption isotherms for MOFs similar to training data but cannot reliably generate novel frameworks in unexplored regions of topology space where the most impactful materials may reside.
The MatterSpace Approach
How MatterSpace generates for metal-organic frameworks
MatterSpace Lattice generates novel MOF topologies by navigating the joint space of metal nodes, organic linkers, and framework connectivity under constraints on pore geometry, thermal stability, and target adsorption properties. Specify gas selectivity requirements, working capacity targets, and stability conditions, and Lattice constructs frameworks satisfying all constraints with enforced structural validity.
The MOF domain pack encodes reticular chemistry principles, pore geometry analysis, gas-framework interaction models, and thermal stability prediction. Users define application requirements — target gas pair selectivity, minimum working capacity, operating temperature range — and Lattice generates MOF candidates with predicted adsorption isotherms and stability assessments.
Constraint-Based Generation
Specify what the output must satisfy. MatterSpace constructs candidates that meet all constraints simultaneously.
Valid by Construction
Every output satisfies physical laws, stability criteria, and domain constraints — no post-hoc filtering needed.
MatterSpace Lattice
Powered by a domain-specific generation engine with physics-aware priors and adaptive dynamics control.
Generation Output
What MatterSpace generates
- Novel MOF topologies with pore geometry specifications
- Gas separation selectivity predictions for target gas pairs
- Catalytic MOF candidates with active site designs
- Thermally stable framework designs with decomposition predictions
Key Differentiators
Why MatterSpace is different
MatterSpace Lattice generates MOF topologies that go beyond enumeration of known building block combinations, exploring novel connectivity patterns and linker geometries that reticular chemistry databases cannot contain. Framework stability under operating conditions is enforced during generation, ensuring every candidate is predicted to maintain structural integrity under target application conditions.
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Whether you are exploring metal-organic frameworks for the first time or scaling an existing research programme, MatterSpace generates novel candidates that satisfy your constraints by construction.
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