Issue 16, 2023

Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane

Abstract

Machine learning (ML)-assisted catalyst investigations for oxidative coupling of methane (OCM) are assessed using published datasets that include literature data reported by different research teams, along with systematic high-throughput screening (HTS) data. Support vector regression (SVR) is performed on the selected 2842 data points. The first SVR leads to eight catalysts with C2 yields higher than 15.0% under the current reaction conditions, but the second attempt with the updated dataset including the first validation results does not improve the prediction because of spatial shrinkage. The Bayesian optimization processes also start with datasets of 3335 data points, and are considered for three cycles using the updated dataset. Repeating the Bayesian processes certainly improves the C2 yields observed in the validation results, but the convergence of the elements presents another issue. Accordingly, data-driven catalyst investigations involve a different set of defect issues from the conventional style of catalyst investigations. The unveiling of issues in the highly active OCM catalyst investigation by ML engineering conducted for this study is intended to clarify future challenging subjects for ML-assisted research innovations. Actions to proactively discover the encounters with serendipity to broaden the scope of the material survey area using ML approaches and/or working with the researcher's intuition can increase the possibility of fortuitous discoveries and the achievement of desired outcomes.

Graphical abstract: Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane

Supplementary files

Article information

Article type
Paper
Submitted
29 Apr 2023
Accepted
26 Mai 2023
First published
01 Jun 2023
This article is Open Access
Creative Commons BY license

Catal. Sci. Technol., 2023,13, 4646-4655

Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane

S. Nishimura, X. Li, J. Ohyama and K. Takahashi, Catal. Sci. Technol., 2023, 13, 4646 DOI: 10.1039/D3CY00596H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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