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.

A tri-layer approach to controlling nanopore formation in oxide supports

A novel tri-layer approach for immobilizing metal nanoparticles in SiO2 supports is presented.In this work,we show that under rapid heating to temperatures of approximately 1,000 ℃,metal nanoparticles less than 15 nm in size will entrench in the SiO2 layer on a silicon wafer to create pores as deep as 250 nm.We studied and characterized this entrenching behavior and subsequent nanopore formation for a wide variety of metal nanoparticles,including Au,Ag,Pt,Pd,and Cu.We also demonstrate that an Al2O3 layer acts as a barrier to such pore formation.Thus,by creating a tri-layer architecture consisting of SiO2 on Al2O3 on silicon wafers,we can control the depth to which nanoparticles entrench between 3-5 nm.This small range allows one to entrench particles for the purpose of immobilization but still present them above the surface.The two advances of moving into the sub-15 nm size regime and of controlled particle immobilization through entrenchment have important implications in studying site-isolated and stabilized metal nanoparticles for applications in sensing,separations,and catalysis.