A Social Network-Guided Approach to Machine Learning for Metal-Organic Framework Property Prediction

The number of new materials and applications of these materials is experiencing rapid growth. ‎Today, increased computational power and the established use of automated machine learning ‎approaches make data science tools available, which provide an overview of the chemical space, ‎support the choice of appropriate materials, and predict specific properties of materials for the ‎desired application. Among the different data science tools, graph theory approaches, where data ‎generated from numerous real-world applications are represented as a graph (network) of ‎connected objects, has been widely used in a variety of scientific fields such as social sciences, ‎health informatics, biological sciences, agricultural sciences, and economics. In this work, we ‎describe applying a particular graph theory approach, social network analysis (SNA), to the metal-organic framework (MOF). To demonstrate MOF materials, we construct a social network called ‎MOFSocialNet from geometrical MOFs descriptors in the CoRE-MOFs database. The MOFSocialNet ‎is an undirected, weighted, and heterogeneous social network; following the construction of this ‎graph, a set of social network analysis processes is conducted to extract valuable knowledge from ‎the MOFs data using graph machine learning algorithms. Community detection is one of the well-known SNA techniques employed on the MOFSocialNet to extract the most similar MOF ‎communities. To evaluate whether the properties of new MOFs can be predicted using MOF ‎communities, we randomly chose three from the CoRE MOFs database. For these MOFs, we ‎excluded the crystal density as input during featurization and placed the MOFs within the ‎MOFSocialNet. The crystal density of the new MOFs is predicted by simply averaging the crystal ‎density of the ten nearest neighbors. ‎ Additionally, communities extracted from MOFSocialNet can be leveraged to predict MOF gas ‎adsorption properties for CO2 and CH4.

Photoinduced growth of Cu nanoparticles on ZnO from CuCl2 in methanol

Cu nanoparticles were formed on surface of nano-ZnO by UV light induced photoreduction of CuCl2 in methanol solution suspended with ZnO nanoparticles. By controlling the reaction conditions, the average size of the produced copper nanocrystal can be fine-tuned in the range of 10–200 nm. At constant UV irradiation, the Cu nanocrystals gradually grew up as the initial concentration of copper cation was increased, showing that the in situ formed Cu nanoparticles act as a bridge to facilitate the transferring of photoexcited electrons from ZnO surface to Cu2+ in solution. A Redox property was also proved for the Cu nanoparticles.Alexander von Humboldt Foundation, Brain Korea 21 Foundation and DFG-SFB 558 are gratefully acknowledge

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A new class of epitaxial porphyrin metal-organic framework thin films with extremely high photocarrier generation efficiency: promising materials for all-solid-state solar cells

We demonstrate the fabrication of a new class of epitaxial porphyrin metal-organic framework thin films whose photophysical properties can be tuned by the introduction of electron-donating diphenylamine (DPA) groups into the porphyrin skeleton. The attachment of DPA groups results in strongly improved absorption characteristics, yielding the highest photocarrier generation efficiency reported so far. DFT calculations identify a shift of the charge localization pattern in the VBM (lowest unoccupied molecular orbital), confirming that the introduction of the DPA groups is the main reason for the shift of the optical absorption spectrum and the improved photocurrent generation