Machine learning for heterogeneous catalyst design and discovery

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

doi:10.1002/aic.16198

Machine learning for heterogeneous catalyst design and discovery

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

Research output: Contribution to journal › Article › peer-review

Original language	English (US)
Pages (from-to)	2311-2323
Number of pages	13
Journal	AIChE Journal
Volume	64
Issue number	7
DOIs	https://doi.org/10.1002/aic.16198
State	Published - Jul 2018
Externally published	Yes

Bibliographical note

Funding Information:
The authors thank Saswata Bhattacharya, Sergey Levchenko, Suljo Linic, Runhai Ouyang, and Matthias Scheffler for helpful discussions about machine learning for catalysis. B.R.G acknowledges start-up funding from University of Michigan, Ann Arbor. C.S. gratefully acknowledges funding through a postdoctoral fellowship by the Alexander von Humboldt Foundation.

Keywords

compressed sensing
computational catalysis
data mining
heterogeneous catalysis
machine learning

Publisher link

10.1002/aic.16198

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@article{87612dcd35594d26a44293a8f93ceabc,

title = "Machine learning for heterogeneous catalyst design and discovery",

keywords = "compressed sensing, computational catalysis, data mining, heterogeneous catalysis, machine learning",

author = "Goldsmith, \{Bryan R.\} and Jacques Esterhuizen and Liu, \{Jin Xun\} and Bartel, \{Christopher J.\} and Christopher Sutton",

note = "Funding Information: The authors thank Saswata Bhattacharya, Sergey Levchenko, Suljo Linic, Runhai Ouyang, and Matthias Scheffler for helpful discussions about machine learning for catalysis. B.R.G acknowledges start-up funding from University of Michigan, Ann Arbor. C.S. gratefully acknowledges funding through a postdoctoral fellowship by the Alexander von Humboldt Foundation.",

year = "2018",

month = jul,

doi = "10.1002/aic.16198",

language = "English (US)",

volume = "64",

pages = "2311--2323",

journal = "AIChE Journal",

issn = "0001-1541",

publisher = "American Institute of Chemical Engineers",

number = "7",

}

TY - JOUR

T1 - Machine learning for heterogeneous catalyst design and discovery

AU - Goldsmith, Bryan R.

AU - Esterhuizen, Jacques

AU - Liu, Jin Xun

AU - Bartel, Christopher J.

AU - Sutton, Christopher

N1 - Funding Information: The authors thank Saswata Bhattacharya, Sergey Levchenko, Suljo Linic, Runhai Ouyang, and Matthias Scheffler for helpful discussions about machine learning for catalysis. B.R.G acknowledges start-up funding from University of Michigan, Ann Arbor. C.S. gratefully acknowledges funding through a postdoctoral fellowship by the Alexander von Humboldt Foundation.

PY - 2018/7

Y1 - 2018/7

KW - compressed sensing

KW - computational catalysis

KW - data mining

KW - heterogeneous catalysis

KW - machine learning

UR - https://www.scopus.com/pages/publications/85047489332

UR - https://www.scopus.com/pages/publications/85047489332#tab=citedBy

U2 - 10.1002/aic.16198

DO - 10.1002/aic.16198

M3 - Article

AN - SCOPUS:85047489332

SN - 0001-1541

VL - 64

SP - 2311

EP - 2323

JO - AIChE Journal

JF - AIChE Journal

IS - 7

ER -

Machine learning for heterogeneous catalyst design and discovery

Bibliographical note

Keywords

Publisher link

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