Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials.

ABSTRACT: Metal-organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms. However, the continuously increasing sizes of MOF datasets and the complicated relationships between MOFs and their gas adsorption capacities make deep learning a suitable candidate to handle such big data with increased computational power and accuracy. In this study, we developed models for predicting gas adsorption capacities of MOFs usi

SUBMITTER: Guo W 

PROVIDER: S-EPMC9565823 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature