A framework for processing adsorption data and isotherm fitting
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Updated
Jun 25, 2024 - Python
A framework for processing adsorption data and isotherm fitting
An Active learning algorithm for multi-component adsorption prediction in MOF
Streamline the process of adsorption modeling for researchers, by automating the fitting of theoretical adsorption models to empirical isotherm data
Use machine learning to harness prediction of chemicals adsorption on adsorbent materials, using data from the NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials
Collect adsorption isotherm data from the NIST/ARPA-E Database
BET surface area analysis from adsorption data
Series of simulations analyzing common chemical engineering, thermodynamic, and kinetic phenomena
A collection of Python code used for carbon dioxide adsorption analysis
The present algorithm generates sets of atomic structures of adsorbed molecules, considering ridge structures and atoms as spheres of VDW radius (or a fraction of it).
Automatically applies betsi criteria to a group of isotherms, and doesn't give up!
Fit temperature-dependent isotherms to equilibrium data.
Repository containing data extracted from AJ Brown's seminal thesis on adsorption-desorption hysteresis, with particular emphasis on scanning isotherms inside the boundary isotherm curves for Xenon adsorption in Vycor Glass.
A machine learning model based on gradient boosting decision tree for predicting heavy metal adsorption in soil.
Tools for generating parameters for helium on uniaxially strained graphene simulations using quantum Monte Carlo software hosted at https://code.delmaestro.org and plots of the helium graphene interaction.
Sequential design of adsorption simulation for small molecule adsorption in a MOF
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