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

Journal Article · Mon Jun 11 00:00:00 EDT 2018 · Molecular Systems Design & Engineering

DOI:https://doi.org/10.1039/C8ME00005K· OSTI ID:1436892

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

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

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

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Sponsoring Organization:: USDOE

Grant/Contract Number:: FOA-0001618/DESC00117076

OSTI ID:: 1436892

Journal Information:: Molecular Systems Design & Engineering, Journal Name: Molecular Systems Design & Engineering Vol. 3 Journal Issue: 3; ISSN 2058-9689

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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Cited by: 19 works

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References (22)

Combinatorial Experimentation and Materials Informatics Takeuchi, Ichiro; Lippmaa, Mikk; Matsumoto, Yuji MRS Bulletin, Vol. 31, Issue 12 https://doi.org/10.1557/mrs2006.228	journal	December 2006
High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates Ling, Julia; Hutchinson, Maxwell; Antono, Erin Integrating Materials and Manufacturing Innovation, Vol. 6, Issue 3 https://doi.org/10.1007/s40192-017-0098-z	journal	July 2017
High-throughput determination of structural phase diagram and constituent phases using GRENDEL Kusne, A. G.; Keller, D.; Anderson, A. Nanotechnology, Vol. 26, Issue 44 https://doi.org/10.1088/0957-4484/26/44/444002	journal	October 2015
Generating Information-Rich High-Throughput Experimental Materials Genomes using Functional Clustering via Multitree Genetic Programming and Information Theory Suram, Santosh K.; Haber, Joel A.; Jin, Jian ACS Combinatorial Science, Vol. 17, Issue 4 https://doi.org/10.1021/co5001579	journal	March 2015
Microstructure-based knowledge systems for capturing process-structure evolution linkages Brough, David B.; Wheeler, Daniel; Warren, James A. Current Opinion in Solid State and Materials Science, Vol. 21, Issue 3 https://doi.org/10.1016/j.cossms.2016.05.002	journal	June 2017
Knowledge Extraction from Atomically Resolved Images Vlcek, Lukas; Maksov, Artem; Pan, Minghu ACS Nano, Vol. 11, Issue 10 https://doi.org/10.1021/acsnano.7b05036	journal	October 2017
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set Kresse, G.; Furthmüller, J. Computational Materials Science, Vol. 6, Issue 1, p. 15-50 https://doi.org/10.1016/0927-0256(96)00008-0	journal	July 1996
Discovery of a new catalytically active and selective zeolite (ITQ-30) by high-throughput synthesis techniques Corma, A.; Diazcabanas, M.; Moliner, M. Journal of Catalysis, Vol. 241, Issue 2 https://doi.org/10.1016/j.jcat.2006.04.036	journal	July 2006
Metal hydride hydrogen compressors: A review Lototskyy, M. V.; Yartys, V. A.; Pollet, B. G. International Journal of Hydrogen Energy, Vol. 39, Issue 11 https://doi.org/10.1016/j.ijhydene.2014.01.158	journal	April 2014
The application of Principal Component Analysis to materials science data Suh, Changwon; Rajagopalan, Arun; Li, Xiang Data Science Journal, Vol. 1 https://doi.org/10.2481/dsj.1.19	journal	January 2002
Data mining for materials: Computational experiments with $A B$ compounds Saad, Yousef; Gao, Da; Ngo, Thanh Physical Review B, Vol. 85, Issue 10 https://doi.org/10.1103/PhysRevB.85.104104	journal	March 2012
Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns Bunn, Jonathan Kenneth; Hu, Jianjun; Hattrick-Simpers, Jason R. JOM, Vol. 68, Issue 8 https://doi.org/10.1007/s11837-016-2033-8	journal	July 2016
How Evolutionary Crystal Structure Prediction Works—and Why Oganov, Artem R.; Lyakhov, Andriy O.; Valle, Mario Accounts of Chemical Research, Vol. 44, Issue 3 https://doi.org/10.1021/ar1001318	journal	March 2011
On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets Kusne, Aaron Gilad; Gao, Tieren; Mehta, Apurva Scientific Reports, Vol. 4, Issue 1 https://doi.org/10.1038/srep06367	journal	September 2014
Grand-canonical evolutionary algorithm for the prediction of two-dimensional materials Revard, Benjamin C.; Tipton, William W.; Yesypenko, Anna Physical Review B, Vol. 93, Issue 5 https://doi.org/10.1103/PhysRevB.93.054117	journal	February 2016
A general-purpose machine learning framework for predicting properties of inorganic materials Ward, Logan; Agrawal, Ankit; Choudhary, Alok npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.28	journal	August 2016
Acceptability envelope for metal hydride-based hydrogen storage systems Corgnale, Claudio; Hardy, Bruce J.; Tamburello, David A. International Journal of Hydrogen Energy, Vol. 37, Issue 3 https://doi.org/10.1016/j.ijhydene.2011.07.037	journal	February 2012
Data mining in bioinformatics using Weka Frank, E.; Hall, M.; Trigg, L. Bioinformatics, Vol. 20, Issue 15 https://doi.org/10.1093/bioinformatics/bth261	journal	April 2004
High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory Choudhary, Kamal; Kalish, Irina; Beams, Ryan Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-05402-0	journal	July 2017
Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory Hautier, Geoffroy; Fischer, Christopher C.; Jain, Anubhav Chemistry of Materials, Vol. 22, Issue 12 https://doi.org/10.1021/cm100795d	journal	June 2010
Ternary phase diagrams of the manganese-titanium-iron and the aluminum-titanium-iron systems: A comparison of computer calculations with experiment Dew-Hughes, David; Kaufman, Larry Calphad, Vol. 3, Issue 3 https://doi.org/10.1016/0364-5916(79)90003-8	journal	January 1979
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013

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