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Title: A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials

Abstract

Here we present the results of using techno-economic analysis as constraints for machine learning guided studies of new metal hydride materials.

Authors:
ORCiD logo [1];  [1];  [2]
  1. Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA
  2. Greenway Energy LLC, Aiken, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1436892
Grant/Contract Number:  
FOA-0001618/DESC00117076
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Molecular Systems Design & Engineering
Additional Journal Information:
Journal Name: Molecular Systems Design & Engineering Journal Volume: 3 Journal Issue: 3; Journal ID: ISSN 2058-9689
Publisher:
Royal Society of Chemistry (RSC)
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Hattrick-Simpers, Jason R., Choudhary, Kamal, and Corgnale, Claudio. A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials. United Kingdom: N. p., 2018. Web. doi:10.1039/C8ME00005K.
Hattrick-Simpers, Jason R., Choudhary, Kamal, & Corgnale, Claudio. A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials. United Kingdom. doi:10.1039/C8ME00005K.
Hattrick-Simpers, Jason R., Choudhary, Kamal, and Corgnale, Claudio. Mon . "A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials". United Kingdom. doi:10.1039/C8ME00005K.
@article{osti_1436892,
title = {A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials},
author = {Hattrick-Simpers, Jason R. and Choudhary, Kamal and Corgnale, Claudio},
abstractNote = {Here we present the results of using techno-economic analysis as constraints for machine learning guided studies of new metal hydride materials.},
doi = {10.1039/C8ME00005K},
journal = {Molecular Systems Design & Engineering},
number = 3,
volume = 3,
place = {United Kingdom},
year = {2018},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1039/C8ME00005K

Citation Metrics:
Cited by: 6 works
Citation information provided by
Web of Science

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