Open AccessMachine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures
Author(s) -
Zhao Li,
Benjamin J. Bucior,
Haoyuan Chen,
Maciej Harańczyk,
J. Ilja Siepmann,
Randall Q. Snurr
Publication year - 2021
Publication title -
journal of chemical physics online/the journal of chemical physics/journal of chemical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.071
H-Index - 357
eISSN - 1089-7690
pISSN - 0021-9606
DOI - 10.1063/5.0050823
Subject(s) - adsorption , propane , histogram , metal organic framework , xenon , work (physics) , binary number , monte carlo method , energy (signal processing) , characterization (materials science) , materials science , chemistry , chemical physics , thermodynamics , computer science , nanotechnology , physics , organic chemistry , artificial intelligence , image (mathematics) , mathematics , statistics , arithmetic , quantum mechanics