A framework for processing adsorption data and isotherm fitting

GPU Monte Carlo Simulation Code with a taste of RASPA

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 machine learning model based on gradient boosting decision tree for predicting heavy metal adsorption in soil.

AIM (Adsorption Integrated Modules) is a collection of MATLAB based GUI modules for adsorption isotherm based fixed bed process modelling

A Python package to run adsorption simulation in nanoporous materials using Machine Learning Potentials and other methods powered by ASE

An Active learning algorithm for multi-component adsorption prediction in MOF

BET surface area analysis from adsorption data

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.

Fluid dynamics for chemical applications

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.

Mirror

Fit temperature-dependent isotherms to equilibrium data.

Automatically applies betsi criteria to a group of isotherms, and doesn't give up!

Modeling Extracellular DNA Adsorption Kinetics via the Dynamic Conformation Adsorption Model.

Periodic DFT workflow (GPAW + ASE) for surface slab modeling and site-dependent adsorption energetics on Pt(111).

HTA磁吸多窗口模板

Model hydrogen adsorption on the surface of nanostructures based on the “Random rain” algorithm