Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts-Reference-Cited by-同舟云学术

Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts

Published:2021-06-21 Issue:16 Volume:13 Page:3636-3655
ISSN:1867-3880
Container-title:ChemCatChem
language:en
Short-container-title:ChemCatChem

Author:

Mine Shinya¹^ORCID,Takao Motoshi¹,Yamaguchi Taichi¹,Toyao Takashi¹²^ORCID,Maeno Zen¹^ORCID,Hakim Siddiki S. M. A.¹^ORCID,Takakusagi Satoru¹^ORCID,Shimizu Ken‐ichi¹²^ORCID,Takigawa Ichigaku³⁴^ORCID

Affiliation:

1. Institute for Catalysis Hokkaido University N-21, W-10 Sapporo 001-0021 Japan

2. Elements Strategy Initiative for Catalysis and Batteries Kyoto University, Katsura Kyoto 615-8520 Japan

3. RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi Chuo-ku Tokyo 103-0027 Japan

4. Institute for Chemical Reaction Design and Discovery (WPI-ICReDD) Hokkaido University N-21, W-10 Sapporo 001-0021 Japan

Funder

Japan Society for the Promotion of Science

Publisher

Wiley

Subject

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

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cctc.202100495

Reference93 articles.

1. Machine learning for molecular and materials science

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4. Inverse molecular design using machine learning: Generative models for matter engineering

5. Inverse Design of Solid-State Materials via a Continuous Representation

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