open data sets for machine learning pertaining to porous materials.
MOF = metal-organic framework
COF = covalent organic framework
MOFs: B&W (Paper , Database ), ToBaCCo (Paper , Database , Code ), hMOFs (Paper , Database ), Anderson et al. (Paper , Database ), MOF-5 analogues (Paper , Database ), PORMAKE (Paper , Code )
COFs: Mercado et al. (Paper , Database ), Haranczyk's 3D COF database (Paper , Database )
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
?