Machine-Learning-Guided Morphology Engineering of Nanoscale Metal-Organic Frameworks

Abstract

Summary Controlling morphology of nanocrystals is one of the central tasks of nanoscience. In this work, we studied nanoscale metal-organic frameworks (nMOFs) from Hf-oxo clusters and linear dicarboxylate ligands with the aid of machine-learning methods for data analysis. Ligand solubility and modulator concentration were found to quantitatively predict the growth of nMOFs with a specific morphology, such as ultrathin two-dimensional film, hexagonal nanoplate, octahedron, cuboctahedron, concave octahedron, or hollow octahedron morphology. With these insights, we use epitaxy growth sequences to design nMOFs of desirable nanostructures with enhanced substrate transport and, hence, increased activities for catalytic olefin hydrogenation. This work highlights new opportunities in using machine learning to guide morphology engineering of nMOFs and other nanomaterials.