Predicting hydrogen storage in MOFs via machine learning
Alauddin Ahmed, Donald J. Siegel
Abstract
uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature.
Topics & Concepts
Hydrogen storageComputer scienceMachine learningArtificial intelligenceChemistryHydrogenOrganic chemistryMetal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials ScienceX-ray Diffraction in Crystallography