Interpretable machine learning for knowledge generation in heterogeneous catalysis-Reference-Cited by-同舟云学术

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Interpretable machine learning for knowledge generation in heterogeneous catalysis

Published:2022-03-17 Issue:3 Volume:5 Page:175-184
ISSN:2520-1158
Container-title:Nature Catalysis
language:en
Short-container-title:Nat Catal

Author:

Esterhuizen Jacques A.^ORCID,Goldsmith Bryan R.^ORCID,Linic Suljo^ORCID

Funder

DOE | SC | Basic Energy Sciences

NSF | ENG/OAD | Division of Chemical, Bioengineering, Environmental, and Transport Systems

Publisher

Springer Science and Business Media LLC

Subject

Process Chemistry and Technology,Biochemistry,Bioengineering,Catalysis

Link

https://www.nature.com/articles/s41929-022-00744-z.pdf

Reference85 articles.

1. Vlachos, D. G. in Advances in Chemical Engineering Vol. 30 (ed. Marin, G. B.) 1–61 (Academic, 2005).

2. Goldsmith, B. R., Esterhuizen, J., Liu, J.-X., Bartel, C. J. & Sutton, C. Machine learning for heterogeneous catalyst design and discovery. AlChE J. 64, 2311–2323 (2018).

3. Schlexer Lamoureux, P. et al. Machine learning for computational heterogeneous catalysis. ChemCatChem 11, 3581–3601 (2019).

4. Kitchin, J. R. Machine learning in catalysis. Nat. Catal. 1, 230–232 (2018).

5. Toyao, T. et al. Machine learning for catalysis informatics: recent applications and prospects. ACS Catal. 10, 2260–2297 (2020).

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