Materials and Energy
Metal-Organic Frameworks
Design metal-organic framework structures with target porosity, gas selectivity, and catalytic activity by exploring metal-node, linker, and topology combinations.
The Challenge
Why Metal-Organic Frameworks teams still lose time to invalid candidate work
Combinations of metal nodes, organic linkers, and framework topologies create millions of theoretically possible MOF structures, yet only a small fraction have been synthesized and characterized. Discovery has largely followed reticular chemistry themes: vary the linker length, swap the metal node, or enumerate topologies from known building blocks. Novel framework architectures with potentially superior properties lie outside this incremental loop.
High-throughput screening evaluates gas adsorption for large databases of hypothetical MOFs, but those databases are built from known building blocks and topologies. Machine-learning models predict adsorption isotherms for MOFs resembling the training set but offer limited guidance in unexplored regions of topology space where high-impact materials may reside.
The MatterSpace Approach
How MatterSpace reduces invalid work in 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. Users specify gas selectivity, working-capacity targets, and stability conditions; 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), and Lattice generates 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 MatterSpace, the Universal Generation Engine for Science and Engineering and a goal-driven inverse 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
Lattice goes beyond enumeration of known building-block combinations, exploring novel connectivity patterns and linker geometries outside existing reticular chemistry databases. Framework stability under operating conditions is enforced during generation, so every candidate maintains structural integrity under the target application conditions.
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Put MatterSpace on a real metal-organic frameworks problem
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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