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Computational prediction of hetero-interpenetration in metal-organic frameworks.

Ohmin KwonSanghoon ParkHong-Cai ZhouJihan Kim
Published in: Chemical communications (Cambridge, England) (2018)
We present a computational algorithm that can screen through a database of metal-organic framework structures and identify materials that lead to hetero-interpenetration with targeted porous materials. Two MOFs (IRMOF-1 and IRMOF-8) were selected as target materials and our algorithm identified PCN-68 and PCN-610 as matching candidates for interpenetration. Molecular simulation results indicate that the interpenetrated MOFs possess enhanced methane and hydrogen adsorption properties compared to the parent materials.
Keyphrases
  • metal organic framework
  • machine learning
  • deep learning
  • high throughput
  • emergency department
  • high resolution
  • neural network
  • single molecule
  • carbon dioxide