mukherjee07/Active-Learning-for-multicomponent-adsorption-in-a-MOF
An Active learning algorithm for multi-component adsorption prediction in MOF
This project helps chemical engineers and material scientists efficiently predict how gas mixtures (like CO₂-CH₄, Xe-Kr, or H₂S-CO₂) adsorb onto a Cu-BTC MOF under various conditions. It takes initial simulation data or experimental measurements of gas adsorption and uses active learning to produce a complete adsorption isotherm and error maps. This tool is designed for researchers in porous materials and gas separation.
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Use this if you need to accurately model multicomponent gas adsorption in MOFs (specifically Cu-BTC) with significantly fewer computational simulations or experiments.
Not ideal if you are working with different types of porous materials or gas mixtures, as this tool is specifically tuned for Cu-BTC MOFs and the listed gas combinations.
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Python
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CC0-1.0
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Last pushed
Sep 18, 2024
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