Machine learning for heterogeneous catalyst design and discovery

Goldsmith, Bryan R.; Esterhuizen, Jacques; Liu, Jin‐Xun; Bartel, Christopher J.; Sutton, Christopher A.

doi:10.1002/aic.16198

Item

ITEM ACTIONSEXPORT

Add to Basket

Please note that a newer version of this item is available:
https://pure.mpg.de/pubman/item/item_2599635_8

DetailsSummary

Released

Journal Article

Machine learning for heterogeneous catalyst design and discovery

MPS-Authors

/persons/resource/persons183305

Sutton, Christopher A.
Theory, Fritz Haber Institute, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

Machine Learning for Catalysis Perspective - AIChE Journal - Clean.pdf
(Any fulltext), 881KB

Supplementary Material (public)

There is no public supplementary material available

Citation

Goldsmith, B. R., Esterhuizen, J., Liu, J., Bartel, C. J., & Sutton, C. A. (2018). Machine learning for heterogeneous catalyst design and discovery. AIChE-Journal, 64(7), 2311-2323. doi:10.1002/aic.16198.

Cite as: https://hdl.handle.net/21.11116/0000-0001-81A0-7

Abstract

There is no abstract available