Skip to content

open data sets for machine learning pertaining to porous materials

Notifications You must be signed in to change notification settings

FMcil/porous-material-AI-gym

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

29 Commits
 
 
 
 

Repository files navigation

porous materials AI gym

open data sets for machine learning pertaining to porous materials.

  • MOF = metal-organic framework
  • COF = covalent organic framework

crystal structures

experimental

hypothetical

labeled porous materials for supervised learning

material class target y features x provided? Reference size of data set
MOFs (hypothetical) CO2, N2 adsorption (sim) yes Paper, Database ca. 325,000
MOFs (experimental and hypothetical) Band gaps, density of states, charge densities (sim) yes Paper, Database ca. 18,000
MOFs (experimental) Color (exp) yes Paper, Database ?
COFs (hypothetical) CH4 deliverable capacity (sim) yes, hand-crafted features provided. Paper, Database ca. 70,000
COFs (experimental) CH4, H2, O2, Xe, Kr, H2S adsorption (sim) ? Paper ca. 500

labeled nodes for supervised learning

material class target y Reference size of data set (# materials)
MOFs (experimental) DDEC6 charges on atoms (sim) Paper, Database ca. 3,000
MOFs (experimental and hypothetical) DDEC6/CM5/Bader charges on atoms (sim) Paper, Database ca. 18,000 (DDEC6/CM5), ca. 5,000 (Bader)
MOFs (experimental and hypothetical) Effective bond orders on atoms (sim) Paper, Database ca. 18,000
MOFs (experimental) Formal oxidation states on atoms (exp) Paper, Database ?

About

open data sets for machine learning pertaining to porous materials

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published