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Cited by 254Machine Learning in Materials ScienceMetal-Organic Frameworks: Synthesis and ApplicationsAdvanced Chemical Physics Studies

Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)

Pardakhti, Maryam, Moharreri, Ehsan, Wanik, David, Suib, Steven L., Srivastava, Ranjan·2017·ACS Combinatorial Science
DOI 10.1021/acscombsci.7b00056
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Abstract

Using molecular simulation for adsorbent screening is computationally expensive and thus prohibitive to materials discovery. Machine learning (ML) algorithms trained on fundamental material properties can potentially provide quick and accurate methods for screening purposes. Prior efforts have focused on structural descriptors for use with ML. In this work, the use of chemical descriptors, in addition to structural descriptors, was introduced for adsorption analysis. Evaluation of structural and chemical descriptors coupled with various ML algorithms, including decision tree, Poisson regression, support vector machine and random forest, were carried out to predict methane uptake on hypothetical metal organic frameworks. To highlight their predictive capabilities, ML models were trained on 8% of a data set consisting of 130,398 MOFs and then tested on the remaining 92% to predict methane adsorption capacities. When structural and chemical descriptors were jointly used as ML input, the random forest model with 10-fold cross validation proved to be superior to the other ML approaches, with an R 2 of 0.98 and a mean absolute percent error of about 7%. The training and prediction using the random forest algorithm for adsorption capacity estimation of all 130,398 MOFs took approximately 2 h on a single personal computer, several orders of magnitude faster than actual molecular simulations on high-performance computing clusters.

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  • Low-cost, topological, three-dimensional, one-hot chemical encoding, and geometric descriptors are often employed for this purpose in transition-metal chemistry.

    “Low-cost, topological (5, 7, 32, 92-94), three-dimensional (95-100), one-hot chemical encoding (92, 101), and geometric (e.g., for MOF pores) (102-104) descriptors are often employed for this purpose in transition-metal chemistry.”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [98]

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Authors
Pardakhti, M., Moharreri, E., Wanik, D., Suib, S. L., & Srivastava, R.
Affiliations
University of Connecticut (US)
Corresponding author
Srivastava, Ranjan
ORCID
Venue
ACS Combinatorial Science
Publisher
American Chemical Society (ACS)
Published
2017-09-05
Volume
19
Issue
10
Pages
640-645
ISSN
  • 2156-8944 (ISSN-L)
  • 2156-8952
DOI
10.1021/acscombsci.7b00056
OpenAlex
W2742477205
Cited by
254
References
42
Citations by year
2026: 23 · 2025: 37 · 2024: 38 · 2023: 35 · 2022: 25 · 2021: 38 · 2020: 34 · 2019: 17 · 2018: 7
FWCI
6.69
Type
journal article
Language
en
Open access
closed
Locations
  • ACS Combinatorial Science (publishedVersion)
  • PubMed (publishedVersion)
Funders
University of Connecticut
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords
metal-organic frameworks, methane adsorption, adsorption capacity prediction, molecular descriptors, structural descriptors, random forest, machine learning screening, materials discovery, gas storage
MeSH terms
Machine Learning, Adsorption, Algorithms, Computer Simulation, Metals, Organometallic Compounds, Software

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