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Due the alarming rate of climate change, the implementation of efficientCO$_2$ capture has become crucial. This project aims to create an algorithmthat predicts the uptake of CO$_2$ adsorbing Metal-Organic Frameworks (MOFs) byusing Machine Learning. These values will in turn gauge the efficiency of theseMOFs and provide scientists who are looking to maximize the uptake a way toknow whether or not the MOF is worth synthesizing. This algorithm will saveresources such as time and equipment as scientists will be able to disregardhypothetical MOFs with low efficiencies. In addition, this paper will alsohighlight the most important features within the data set. This research willcontribute to enable the rapid synthesis of CO$_2$ adsorbing MOFs.

Predicting the Efficiency of CO$_2$ Sequestering by Metal Organic  Frameworks Through Machine Learning Analysis of Structural and Electronic  Properties