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Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage.
Fanourgakis, George S; Gkagkas, Konstantinos; Froudakis, George.
Afiliação
  • Fanourgakis GS; Department of Chemistry, University of Crete, Voutes Campus, GR-70013 Heraklion, Crete, Greece.
  • Gkagkas K; Material Engineering Division, Toyota Motor Europe NV/SA, Technical Center, Hoge Wei 33B, 1930 Zaventem, Belgium.
  • Froudakis G; Department of Chemistry, University of Crete, Voutes Campus, GR-70013 Heraklion, Crete, Greece.
J Chem Phys ; 156(5): 054103, 2022 Feb 07.
Article em En | MEDLINE | ID: mdl-35135256
In the field of materials science, the main objective of predictive models is to provide scientists with reliable tools for fast and accurate identification of new materials with exceptional properties. Over the last few years, machine learning methods have been extensively used for the study of the gas-adsorption in nanoporous materials as an efficient alternative of molecular simulations and experiments. In several cases, the accuracy of the constructed predictive models for unknown materials is extremely high. In this study, we explored the adsorption of methane by metal organic frameworks (MOFs) and concluded that many top-performing materials often deviate significantly from the known materials used for the training of the machine learning algorithms. In such cases, the predictions of the machine learning algorithms may not be adequately accurate. For lack of the required appropriate data, we put forth a simple approach for the construction of artificial MOFs with the desired superior properties. Incorporation of such data during the training phase of the machine learning algorithms improves the predictions outstandingly. In some cases, over 96% of the unknown top-performing materials are successfully identified.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article