Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures

Ryther Anderson; Diego Gomez-Gualdron

doi:10.17605/OSF.IO/7DGVY

Title	Authors

Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures

Contributors:

Date created: | Last Updated:

: DOI | ARK

Creating DOI. Please wait...

Create DOI

Category: Project

Description: A repository of raw data and MOF structures used in our recent work entitled "Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures."

Projects
Registrations

Results: All Projects Results: My Projects Results: All Registrations Results: My Registrations

Files

Loading files...

Citation

Recent Activity

Loading logs...

OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.

This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.

Create an Account Learn More Hide this message

Main content

Links to this project

Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures

Link other OSF projects

Files

Citation

Recent Activity

Start managing your projects on the OSF today.