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 journalArticlepeer-review

266 Scopus citations
Original languageEnglish (US)
Pages (from-to)2311-2323
Number of pages13
JournalAIChE Journal
Volume64
Issue number7
DOIs
StatePublished - Jul 2018
Externally publishedYes

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

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