Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Andrew S. Rosen*, Shaelyn M. Iyer, Debmalya Ray, Zhenpeng Yao, Alán Aspuru-Guzik, Laura Gagliardi, Justin M. Notestein, Randall Q. Snurr

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The modular nature of metal–organic frameworks (MOFs) enables synthetic control over their physical and chemical properties, but it can be difficult to know which MOFs would be optimal for a given application. High-throughput computational screening and machine learning are promising routes to efficiently navigate the vast chemical space of MOFs but have rarely been used for the prediction of properties that need to be calculated by quantum mechanical methods. Here, we introduce the Quantum MOF (QMOF) database, a publicly available database of computed quantum-chemical properties for more than 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how machine learning models trained on the QMOF database can be used to rapidly discover MOFs with targeted electronic structure properties, using the prediction of theoretically computed band gaps as a representative example. We conclude by highlighting several MOFs predicted to have low band gaps, a challenging task given the electronically insulating nature of most MOFs.

Original languageEnglish (US)
Pages (from-to)1578-1597
Number of pages20
JournalMatter
Volume4
Issue number5
DOIs
StatePublished - May 5 2021

Keywords

  • MAP3: Understanding
  • artificial intelligence
  • band gap
  • database
  • density functional theory
  • electronic structure
  • high-throughput screening
  • machine learning
  • materials discovery
  • metal–organic framework
  • quantum chemistry

ASJC Scopus subject areas

  • Materials Science(all)

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