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

Hattrick-Simpers, Jason R.; Choudhary, Kamal; Corgnale, Claudio

doi:10.1039/C8ME00005K

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:

^[1]; Choudhary, Kamal ^[1]; Corgnale, Claudio ^[2]

Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA
Greenway Energy LLC, Aiken, USA

Publication Date:: 2018-06-11

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.

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                    Hattrick-Simpers, Jason R., Choudhary, Kamal, & Corgnale, Claudio. A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials.  United Kingdom.  https://doi.org/10.1039/C8ME00005K

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                    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.  https://doi.org/10.1039/C8ME00005K.

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@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}

}

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

Abstract

Citation Formats

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Discovery of a new catalytically active and selective zeolite (ITQ-30) by high-throughput synthesis techniques
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Metal hydride hydrogen compressors: A review
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The application of Principal Component Analysis to materials science data
journal, January 2002

Data mining for materials: Computational experiments with $A B$ compounds
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Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns
journal, July 2016

How Evolutionary Crystal Structure Prediction Works—and Why
journal, March 2011

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets
journal, September 2014

Grand-canonical evolutionary algorithm for the prediction of two-dimensional materials
journal, February 2016

A general-purpose machine learning framework for predicting properties of inorganic materials
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Acceptability envelope for metal hydride-based hydrogen storage systems
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Data mining in bioinformatics using Weka
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High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory
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