Gas adsorption meets deep learning: voxelizing the potential energy surface of metal-organic frameworks.

ABSTRACT: Intrinsic properties of metal-organic frameworks (MOFs), such as their ultra porosity and high surface area, deem them promising solutions for problems involving gas adsorption. Nevertheless, due to their combinatorial nature, a huge number of structures is feasible which renders cumbersome the selection of the best candidates with traditional techniques. Recently, machine learning approaches have emerged as efficient tools to deal with this challenge, by allowing researchers to rapidly screen large databases of MOFs via predictive models. The performance of the latter is tightly tied to the mathematical representation of a material, thus necessitating the use of informative descriptors. In this work, a generalized framework to predict gaseous adsorption properties is presented, using as o

SUBMITTER: Sarikas AP 

PROVIDER: S-EPMC10817925 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature