GPU Monte Carlo Simulation Code with a taste of RASPA
A framework for processing adsorption data and isotherm fitting
GOMC - GPU Optimized Monte Carlo is a parallel molecular simulation code designed for high-performance simulation of large systems
Input script for Monte Carlo (GCMC) simulations
A Python package to run adsorption simulation in nanoporous materials using Machine Learning Potentials and other methods powered by ASE
AIM (Adsorption Integrated Modules) is a collection of MATLAB based GUI modules for adsorption isotherm based fixed bed process modelling
A machine learning model based on gradient boosting decision tree for predicting heavy metal adsorption in soil.
An Active learning algorithm for multi-component adsorption prediction in MOF
BET surface area analysis from adsorption data
Fluid dynamics for chemical applications
Python module for adsorption structure generation and potential energy surface scans of molecule–surface systems using Bayesian Optimization (BOSS)-guided structure generation and MACE machine-learned potentials.
Streamline adsorption modeling by automatically fitting theoretical adsorption models to empirical isotherm data and by training a machine learning model on adsorption isotherms from the NIST and ARPA-E databases to predict uptake as a function of pressure.
Fit temperature-dependent isotherms to equilibrium data.
Automatically applies betsi criteria to a group of isotherms, and doesn't give up!
TeNeT is a tensor-network renormalization code for the statistical mechanics of lattice models. The main application is adsorption of molecules on surfaces.
Automated screening of green corrosion inhibitors: DFT/QM reactivity descriptors, an adsorption estimate, a ranking, and a self-contained HTML report, from a molecule name or SMILES and a metal. Free software only.
Modeling Extracellular DNA Adsorption Kinetics via the Dynamic Conformation Adsorption Model.
Model hydrogen adsorption on the surface of nanostructures based on the “Random rain” algorithm
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