kwankoravich/capturing_CO2_working_cap_MOFs
This project is a part of competition of Thailand Machine Learning for Chemistry Competition (TMLCC 2021) regarding predict the gas adsorption ability of metal-organic frameworks using machine learning.
This project helps chemical researchers and materials scientists predict how well a Metal-Organic Framework (MOF) can capture CO2. By providing various structural and chemical properties of a MOF, it estimates its 'CO2 working capacity,' indicating its potential for carbon capture. This tool is designed for researchers developing new MOF materials to combat climate change.
No commits in the last 6 months.
Use this if you are a materials scientist or chemist needing to quickly evaluate the CO2 adsorption potential of new MOF designs without extensive lab testing.
Not ideal if you need to understand the fundamental quantum mechanical interactions or design MOFs at an atomic level rather than predicting their macroscopic performance.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Oct 20, 2021
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