de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
 
 
DownloadE-Mail
  Machine learning for heterogeneous catalyst design and discovery

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.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0001-81A0-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-AD39-D
Genre: Journal Article

Files

show Files
hide Files
:
Machine Learning for Catalysis Perspective - AIChE Journal - Clean.pdf (Any fulltext), 881KB
 
File Permalink:
-
Description:
-
Visibility:
Private (embargoed till 2019-05-07)
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
2018
Copyright Info:
AIChE
License:
-

Locators

show

Creators

show
hide
 Creators:
Goldsmith, Bryan R.1, Author
Esterhuizen, Jacques1, Author
Liu, Jin‐Xun1, Author
Bartel, Christopher J.2, Author
Sutton, Christopher A.3, Author              
Affiliations:
1Dept. of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109‐2136, escidoc:persistent22              
2Dept. of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, escidoc:persistent22              
3Theory, Fritz Haber Institute, Max Planck Society, escidoc:634547              

Content

show

Details

show
hide
Language(s): eng - English
 Dates: 2018-05-072018-07
 Publication Status: Published in print
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1002/aic.16198
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: AIChE-Journal
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York : American Institute of Chemical Engineers (AIChE)
Pages: 13 Volume / Issue: 64 (7) Sequence Number: - Start / End Page: 2311 - 2323 Identifier: ISSN: 0001-1541
CoNE: http://pubman.mpdl.mpg.de/cone/journals/resource/954925372782