Modeling of CO₂ adsorption capacity by porous metal organic frameworks using advanced decision tree-based models.

ABSTRACT: In recent years, metal organic frameworks (MOFs) have been distinguished as a very promising and efficient group of materials which can be used in carbon capture and storage (CCS) projects. In the present study, the potential ability of modern and powerful decision tree-based methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) was investigated to predict carbon dioxide adsorption by 19 different MOFs. Reviewing the literature, a comprehensive databank was gathered including 1191 data points related to the adsorption capacity of different MOFs in various conditions. The inputs of the implemented models were selected as temperature (K), pressure (bar), specific surface area (m²/g)

SUBMITTER: Abdi J 

PROVIDER: S-EPMC8714819 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature