Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting.

ABSTRACT: Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H₂O from N₂ and O₂ for 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) and 137,953 hypothetical MOFs (hMOFs). Through the univariate analysis of MOF structure-performance relationships, Q_st is shown to be a key descriptor. Moreover, three ML algorithms (random forest, gradient boosted regression trees, and neighbor component analysis (NCA)) are applied to hunt for the complicated interrelation between six descriptors and performance. After the opt

SUBMITTER: Li L 

PROVIDER: S-EPMC8746952 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature