Mapping the Porous and Chemical Structure-Function Relationships of Trace CH3I Capture by Metal-Organic Frameworks using Machine Learning

Xiaoyu Wu, Yu Che, Linjiang Chen*, Eric Amigues, Ruiyao Wang, Jinghui He, Huilong Dong, Lifeng Ding*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal−organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH3I); as a primary representative of organic iodides, CH3129I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure−function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure−function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure−function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure−function maps are amenable to supervised classification of the performances of MOFs for trace CH3I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies.

Original languageEnglish
Pages (from-to)47209-47221
Number of pages13
JournalACS applied materials & interfaces
Volume14
Issue number41
DOIs
Publication statusPublished - 19 Oct 2022

Keywords

  • GCMC simulation
  • computational materials screening
  • data visualization
  • machine learning
  • metal−organic frameworks
  • methyl iodide capture

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