High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project

Second Demographic Transition

Encyclopedia of Gerontology and Population Agin

Electrografting of calix[4]arenediazonium salts to form versatile robust platforms for spatially controlled surface functionalization

An essential issue in the development of materials presenting an accurately functionalized surface is to achieve control of layer structuring. Whereas the very popular method based on the spontaneous adsorption of alkanethiols on metal faces stability problems, the reductive electrografting of aryldiazonium salts yielding stable interface, struggles with the control of the formation and organization of monolayers. Here we report a general strategy for patterning surfaces using aryldiazonium surface chemistry. Calix[4]tetra-diazonium cations generated in situ from the corresponding tetra-anilines were electrografted on gold and carbon substrates. The well-preorganized macrocyclic structure of the calix[4]arene molecules allows the formation of densely packed monolayers. Through adequate decoration of the small rim of the calixarenes, functional molecules can then be introduced on the immobilized calixarene subunits, paving the way for an accurate spatial control of the chemical composition of a surface at molecular level.SCOPUS: ar.jinfo:eu-repo/semantics/publishe