Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data-Reference-Cited by-同舟云学术

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Revisiting Machine Learning Predictions for Oxidative Coupling of Methane (OCM) based on Literature Data

Published:2020-09-11 Issue:23 Volume:12 Page:5888-5892
ISSN:1867-3880
Container-title:ChemCatChem
language:en
Short-container-title:ChemCatChem

Author:

Nishimura Shun¹^ORCID,Ohyama Junya²,Kinoshita Takaaki³,Dinh Le Son¹,Takahashi Keisuke⁴

Affiliation:

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

2. Faculty of Advanced Science and Technology Kumamoto University Kumamoto 860-8555 Japan

3. Graduate School of Science and Technology Kumamoto University Kumamoto 860-8555 Japan

4. Department of Chemistry Hokkaido University Sapporo 060-8510 Japan

Funder

Japan Science and Technology Agency

Publisher

Wiley

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Catalysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cctc.202001032

Reference81 articles.

1.

2. Towards the computational design of solid catalysts

3. Integrating high-throughput characterization into combinatorial heterogeneous catalysis: unsupervised construction of quantitative structure/property relationship models

4. Heterogeneous catalyst discovery using 21st century tools: a tutorial

5.

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