Deep learning neural network potential for simulating gaseous adsorption in metal–organic frameworks
Chi‐Ta Yang, I. Pandey, Dan Trinh, Chau‐Chyun Chen, Joshua D. Howe, Li‐Chiang Lin
Abstract
This study proposes ab initio neural network force fields with physically motivated features to offer superior accuracy in describing adsorbate–adsorbent interactions of nonpolar (CO 2 ) and polar (H 2 O and CO) molecules in metal–organic frameworks with open-metal sites.
Topics & Concepts
AdsorptionArtificial neural networkMetal-organic frameworkAb initioOrganic moleculesPolarMetalMoleculeChemical polarityArtificial intelligenceComputer scienceMaterials scienceChemistryComputational chemistryNanotechnologyChemical physicsBiological systemPhysical chemistryOrganic chemistryPhysicsAstronomyBiologyMetal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials SciencePhase Equilibria and Thermodynamics