Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane-Reference-Cited by-同舟云学术

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

Published:2023 Issue:16 Volume:13 Page:4646-4655
ISSN:2044-4753
Container-title:Catalysis Science & Technology
language:en
Short-container-title:Catal. Sci. Technol.

Author:

Nishimura Shun¹^ORCID,Li Xinyue¹,Ohyama Junya²^ORCID,Takahashi Keisuke³^ORCID

Affiliation:

1. Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi 923-1292, Japan

2. Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan

3. Department of Chemistry, Hokkaido University, North-10, West-8, Sapporo 060-0810, Japan

Abstract

Unveiling current issues in the investigation of highly-active heterogeneous catalysts using machine learning engineering techniques was discussed in the case of oxidative coupling of methane with support vector regression and Bayesian optimization.

Funder

Japan Science and Technology Agency

Publisher

Royal Society of Chemistry (RSC)

Subject

Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CY/D3CY00596H

Reference55 articles.

1. Organic reaction mechanism classification using machine learning

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