In Silico High-Throughput Design and Prediction of Structural and Electronic Properties of Low-Dimensional Metal-Organic Frameworks

Zeyu Zhang, Dylan S. Valente, Yuliang Shi, Dil K. Limbu, Mohammad R. Momeni, Farnaz A. Shakib

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The advent of π-stacked layered metal-organic frameworks (MOFs), which offer electrical conductivity on top of permanent porosity and high surface area, opened up new horizons for designing compact MOF-based devices such as battery electrodes, supercapacitors, and spintronics. Permutation of structural building blocks, including metal nodes and organic linkers, in these electrically conductive (EC) materials, results in new systems with unprecedented and unexplored physical and chemical properties. With the ultimate goal of providing a platform for accelerated material design and discovery, here we lay the foundations for the creation of the first comprehensive database of EC-MOFs with an experimentally guided approach. The first phase of this database, coined EC-MOF/Phase-I, is composed of 1,057 bulk and monolayer structures built by all possible combinations of experimentally reported organic linkers, functional groups, and metal nodes. A high-throughput screening (HTS) workflow is constructed to implement density functional theory calculations with periodic boundary conditions to optimize the structures and calculate some of their most relevant properties. Because research and development in the area of EC-MOFs has long been suffering from the lack of appropriate initial crystal structures, all of the geometries and property data have been made available for the use of the community through an online platform that was developed during the course of this work. This database provides comprehensive physical and chemical data of EC-MOFs as well as the convenience of selecting appropriate materials for specific applications, thus accelerating the design and discovery of EC-MOF-based compact devices.

Original languageEnglish (US)
JournalACS Applied Materials and Interfaces
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • General Materials Science

Keywords

  • computationally ready databases
  • electrical conductivity
  • high-throughput screening
  • low-dimensional materials
  • metal−organic frameworks (MOFs)

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