Machine Learning for Catalysis Informatics: Recent Applications and Prospects-Reference-Cited by-同舟云学术

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Published:2019-12-16 Issue:3 Volume:10 Page:2260-2297
ISSN:2155-5435
Container-title:ACS Catalysis
language:en
Short-container-title:ACS Catal.

Author:

Toyao Takashi¹²^ORCID,Maeno Zen¹,Takakusagi Satoru¹,Kamachi Takashi²³^ORCID,Takigawa Ichigaku⁴⁵^ORCID,Shimizu Ken-ichi¹²^ORCID

Affiliation:

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

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

3. Department of Life, Environment and Materials Science, Fukuoka Institute of Technology, 3-30-1Wajiro-Higashi, Higashi-ku, Fukuoka 811-0295, Japan

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

5. Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan

Funder

Ministry of Education, Culture, Sports, Science and Technology

Core Research for Evolutional Science and Technology

Publisher

American Chemical Society (ACS)

Subject

Catalysis,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acscatal.9b04186

Reference253 articles.

1. Heterogeneous Catalysis

2. Towards the computational design of solid catalysts

3. Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis

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

5. Theory of surface chemistry and reactivity of reducible oxides

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