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Polygence Scholar2021
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Mahati MANDA

Basis Independent Silicon ValleyClass of 2022

About

Projects

  • Predicting the Efficiency of CO2 capture of Metal Organic Frameworks through Analysis of Structural and Electronic Properties and Utilization of Machine Learning with mentor Christine (Sept. 16, 2021)

Mahati's Symposium Presentation

Project Portfolio

Predicting the Efficiency of CO2 capture of Metal Organic Frameworks through Analysis of Structural and Electronic Properties and Utilization of Machine Learning

Started June 28, 2021

Abstract or project description

Metal Organic Frameworks are porous substances that can be used to capture and store different gases. As Climate Change is posing a greater threat to the environment, it is imperative that efficient CO2-Capturing MOFs should be created and deployed. Machine Learning can be used to predict the efficiency of CO2-Capturing MOFs while considering multiple variables. In this report, properties that make CO2-Capturing MOFs efficient will be found through the utilization of Machine Learning techniques. This will allow scientists to be able to save resources and create CO2 Capturing MOFs with high efficiencies.