CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo
  Metal Organic Frameworks (MOFs) for Carbon Capture
Neel Redkar
Over the past decade, climate change has become an increasing problem with
one of the major contributing factors being carbon dioxide (CO2) emissions;
almost 51% of total US carbon emissions are from factories. Current materials
used in CO2 capture are lacking either in efficiency, sustainability, or cost.
  Electrocatalysis of CO2 is a new approach where CO2 can be reduced and the
components used industrially as fuel, saving transportation costs, creating
financial incentives. Metal Organic Frameworks (MOFs) are crystals made of
organo-metals that adsorb, filter, and electrocatalyze CO2. The current
available MOFs for capture & electrocatalysis are expensive to manufacture and
inefficient at capture. The goal therefore is to computationally design a MOF
that can adsorb CO2 and catalyze carbon monoxide & oxygen with low cost.
  A novel active transfer learning neural network was developed, utilizing
transfer learning due to limited available data on 15 MOFs. Using the Cambridge
Structural Database with 10,000 MOFs, the model used incremental mutations to
fit a trained fitness hyper-heuristic function. Eventually, a Selenium MOF
(C18MgO25Se11Sn20Zn5) was converged on. Through analysis of predictions &
literature, the converged MOF was shown to be more effective & more
synthetically accessible than existing MOFs, showing the model had an
understanding of effective electrocatalytic structures in the material space.
This novel network can be implemented for other gas separations and catalysis
applications that have limited training accessible datasets.