DOI: 10.1016/j.commatsci.2018.04.033. Predicting the thermodynamic stability of perovskite oxides using machine learning models

Predicting the thermodynamic stability of perovskite oxides using machine learning models

10.1016/j.commatsci.2018.04.033

Crossref journal-article

Elsevier BV

Computational Materials Science (78)

Authors 3

Wei Li (first)
Ryan Jacobs (additional)
Dane Morgan (additional)

References 43 Referenced 207

Ishihara, T., Perovskite oxide for solid oxide fuel cells, Springer Science & Business Media, 2009. (10.1007/978-0-387-77708-5)
10.1002/aenm.201702708 / Adv. Energy Mater. / Material discovery and design principles for stable, high activity perovskite cathodes for solid oxide fuel cells by Jacobs (2018)
10.1021/acs.chemmater.6b01182 / Chem. Mater. / High-throughput computational screening of perovskites for thermochemical water splitting applications by Emery (2016)
S. Wang et al., Assessing the thermoelectric properties of sintered compounds via high-throughput ab-initio calculations. Phys. Rev. X, 2011. 1(2), 021012. (10.1103/PhysRevX.1.021012)
{'issue': '1', 'key': '10.1016/j.commatsci.2018.04.033_b0025', 'first-page': '011019', 'article-title': 'Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling', 'volume': '4', 'author': 'Carrete', 'year': '2014', 'journal-title': 'Phys. Rev. X'} / Phys. Rev. X / Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling by Carrete (2014)
10.1039/C1EE02717D / Energy Environ. Sci. / Computational screening of perovskite metal oxides for optimal solar light capture by Castelli (2012)
10.1039/C2EE23482C / Energy Environ. Sci. / First principles high throughput screening of oxynitrides for water-splitting photocatalysts by Wu (2013)
10.1039/c2ee22341d / Energy Environ. Sci. / New cubic perovskites for one-and two-photon water splitting using the computational materials repository by Castelli (2012)
10.1038/nmat1752 / Nat Mater / Computational high-throughput screening of electrocatalytic materials for hydrogen evolution by Greeley (2006)
10.1038/nchem.367 / Nat. Chem. / Alloys of platinum and early transition metals as oxygen reduction electrocatalysts by Greeley (2009)
10.1016/j.jeurceramsoc.2007.02.212 / J. Eur. Ceram. Soc. / Prediction of the functional properties of ceramic materials from composition using artificial neural networks by Scott (2007)
10.1103/PhysRevB.93.115104 / Phys. Rev. B / Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques by Lee (2016)
10.1103/PhysRevLett.117.135502 / Phys. Rev. Lett. / Machine learning energies of 2 million elpasolite (A B C 2 D 6) crystals by Faber (2016)
10.1088/1367-2630/15/9/095003 / New J. Phys. / Machine learning of molecular electronic properties in chemical compound space by Montavon (2013)
10.1103/PhysRevB.89.205118 / Phys. Rev. B / How to represent crystal structures for machine learning: Towards fast prediction of electronic properties by Schütt (2014)
10.1021/ct400195d / J. Chem. Theory Comput. / Assessment and validation of machine learning methods for predicting molecular atomization energies by Hansen (2013)
10.1103/PhysRevB.89.134103 / Phys. Rev. B / High-throughput screening of perovskite alloys for piezoelectric performance and thermodynamic stability by Armiento (2014)
10.1103/PhysRevB.89.094104 / Phys. Rev. B / Combinatorial screening for new materials in unconstrained composition space with machine learning by Meredig (2014)
10.1038/npjcompumats.2016.28 / Npj Comput. Mater. / A general-purpose machine learning framework for predicting properties of inorganic materials by Ward (2016)
10.1021/acs.chemmater.7b00156 / Chem. Mater. / Predicting the thermodynamic stability of solids combining density functional theory and machine learning by Schmidt (2017)
10.1039/C4EE03389B / Energy Environ. Sci. / Spinel compounds as multivalent battery cathodes: a systematic evaluation based on ab initio calculations by Liu (2015)
10.1063/1.4812323 / Apl Mater. / Commentary: the materials project: a materials genome approach to accelerating materials innovation by Jain (2013)
10.1007/s10994-006-6226-1 / Mach. Learn. / Extremely randomized trees by Geurts (2006)
10.1080/00401706.1970.10488634 / Technometrics / Ridge regression: biased estimation for nonorthogonal problems by Hoerl (1970)
{'issue': 'Oct', 'key': '10.1016/j.commatsci.2018.04.033_b0125', 'first-page': '2825', 'article-title': 'Scikit-learn: machine learning in Python', 'volume': '12', 'author': 'Pedregosa', 'year': '2011', 'journal-title': 'J. Mach. Learn. Res.'} / J. Mach. Learn. Res. / Scikit-learn: machine learning in Python by Pedregosa (2011)
W. Li, R. Jacobs, D. Morgan, Data and supplemental information for predicting the thermodynamic stability of perovskite oxides using machine learning models. Data in Brief, 2018. (Submitted for publication). (10.1016/j.dib.2018.05.007)
10.1126/sciadv.1600225 / Sci. Adv. / The thermodynamic scale of inorganic crystalline metastability by Sun (2016)
Herr, N. The Sourcebook for Teaching Science. 2007 [cited accessed May 2017; Available from: https://www.csun.edu/science/ref/spreadsheets/.
10.1007/BF01507527 / Naturwissenschaften / Die gesetze der krystallochemie by Goldschmidt (1926)
10.1016/j.jallcom.2003.10.017 / J. Alloys Comp. / Formability of ABO 3 perovskites by Li (2004)
R.D. Shannon, Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides. Acta Crystallograph. Sect. A: Crystal Phys. Diffr. Theoretical General Crystallograp. 32(5) (1976) p. 751–767.
10.1111/j.1467-9868.2010.00740.x / J. Royal Statist. Soc.: Series B (Statistical Methodology) / Stability selection by Meinshausen (2010)
10.1023/A:1012487302797 / Mach. Learn. / Gene selection for cancer classification using support vector machines by Guyon (2002)
10.1103/PhysRevE.69.066138 / Phys. Rev. E / Estimating mutual information by Kraskov (2004)
D.W. Hosmer Jr, S. Lemeshow, R.X. Sturdivant, Applied logistic regression, vol. 398, John Wiley & Sons, 2013. (10.1002/9781118548387)
B. Yegnanarayana, Artificial Neural neTworks, PHI Learning Pvt. Ltd, 2009.
10.1023/A:1018628609742 / Neural Process. Lett. / Least squares support vector machine classifiers by Suykens (1999)
Model evaluation: quantifying the quality of predictions. [cited accessed May 2017; Available from: http://scikit-learn.org/stable/modules/model_evaluation.html.
10.1007/s11837-013-0755-4 / Jom / Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD) by Saal (2013)
10.1021/cm400893e / Chem. Mater. / Data-driven review of thermoelectric materials: performance and resource considerations by Gaultois (2013)
10.3348/kjr.2004.5.1.11 / Kor. J. Radiol. / Receiver operating characteristic (ROC) curve: practical review for radiologists by Park (2004)
10.1021/cm902640j / Chem. Mater. / Materials for solid oxide fuel cells by Jacobson (2009)
10.1016/j.actamat.2003.08.004 / Acta Mater. / Fuel cell materials and components by Haile (2003)

Dates

Type	When
Created	7 years, 4 months ago (April 25, 2018, 6:57 p.m.)
Deposited	4 years, 4 months ago (April 18, 2021, 10:56 p.m.)
Indexed	14 hours, 29 minutes ago (Aug. 27, 2025, 11:34 a.m.)
Issued	7 years, 1 month ago (July 1, 2018)
Published	7 years, 1 month ago (July 1, 2018)
Published Print	7 years, 1 month ago (July 1, 2018)

Funders 2

National Science Foundation 10.13039/100000001
Region: Americas
gov (National government)
Labels4
1. U.S. National Science Foundation
2. NSF
3. US NSF
4. USA NSF
Awards1
1. 1148011
National Science Foundation 10.13039/100000001
Region: Americas
gov (National government)
Labels4
1. U.S. National Science Foundation
2. NSF
3. US NSF
4. USA NSF
Awards1
1. 1148011

BibTeX

@article{Li_2018, title={Predicting the thermodynamic stability of perovskite oxides using machine learning models}, volume={150}, ISSN={0927-0256}, url={http://dx.doi.org/10.1016/j.commatsci.2018.04.033}, DOI={10.1016/j.commatsci.2018.04.033}, journal={Computational Materials Science}, publisher={Elsevier BV}, author={Li, Wei and Jacobs, Ryan and Morgan, Dane}, year={2018}, month=jul, pages={454–463} }

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      "unstructured": "R.D. Shannon, Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides. Acta Crystallograph. Sect. A: Crystal Phys. Diffr. Theoretical General Crystallograp. 32(5) (1976) p. 751\u2013767."
    },
    {
      "issue": "4",
      "key": "10.1016/j.commatsci.2018.04.033_b0160",
      "doi-asserted-by": "crossref",
      "first-page": "417",
      "DOI": "10.1111/j.1467-9868.2010.00740.x",
      "article-title": "Stability selection",
      "volume": "72",
      "author": "Meinshausen",
      "year": "2010",
      "journal-title": "J. Royal Statist. Soc.: Series B (Statistical Methodology)"
    },
    {
      "issue": "1",
      "key": "10.1016/j.commatsci.2018.04.033_b0165",
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      "first-page": "389",
      "DOI": "10.1023/A:1012487302797",
      "article-title": "Gene selection for cancer classification using support vector machines",
      "volume": "46",
      "author": "Guyon",
      "year": "2002",
      "journal-title": "Mach. Learn."
    },
    {
      "issue": "6",
      "key": "10.1016/j.commatsci.2018.04.033_b0170",
      "doi-asserted-by": "crossref",
      "first-page": "066138",
      "DOI": "10.1103/PhysRevE.69.066138",
      "article-title": "Estimating mutual information",
      "volume": "69",
      "author": "Kraskov",
      "year": "2004",
      "journal-title": "Phys. Rev. E"
    },
    {
      "key": "10.1016/j.commatsci.2018.04.033_b0175",
      "doi-asserted-by": "crossref",
      "unstructured": "D.W. Hosmer Jr, S. Lemeshow, R.X. Sturdivant, Applied logistic regression, vol. 398, John Wiley & Sons, 2013.",
      "DOI": "10.1002/9781118548387"
    },
    {
      "key": "10.1016/j.commatsci.2018.04.033_b0180",
      "unstructured": "B. Yegnanarayana, Artificial Neural neTworks, PHI Learning Pvt. Ltd, 2009."
    },
    {
      "issue": "3",
      "key": "10.1016/j.commatsci.2018.04.033_b0185",
      "doi-asserted-by": "crossref",
      "first-page": "293",
      "DOI": "10.1023/A:1018628609742",
      "article-title": "Least squares support vector machine classifiers",
      "volume": "9",
      "author": "Suykens",
      "year": "1999",
      "journal-title": "Neural Process. Lett."
    },
    {
      "key": "10.1016/j.commatsci.2018.04.033_b0190",
      "unstructured": "Model evaluation: quantifying the quality of predictions. [cited accessed May 2017; Available from: http://scikit-learn.org/stable/modules/model_evaluation.html."
    },
    {
      "issue": "11",
      "key": "10.1016/j.commatsci.2018.04.033_b0195",
      "doi-asserted-by": "crossref",
      "first-page": "1501",
      "DOI": "10.1007/s11837-013-0755-4",
      "article-title": "Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD)",
      "volume": "65",
      "author": "Saal",
      "year": "2013",
      "journal-title": "Jom"
    },
    {
      "issue": "15",
      "key": "10.1016/j.commatsci.2018.04.033_b0200",
      "doi-asserted-by": "crossref",
      "first-page": "2911",
      "DOI": "10.1021/cm400893e",
      "article-title": "Data-driven review of thermoelectric materials: performance and resource considerations",
      "volume": "25",
      "author": "Gaultois",
      "year": "2013",
      "journal-title": "Chem. Mater."
    },
    {
      "issue": "1",
      "key": "10.1016/j.commatsci.2018.04.033_b0205",
      "doi-asserted-by": "crossref",
      "first-page": "11",
      "DOI": "10.3348/kjr.2004.5.1.11",
      "article-title": "Receiver operating characteristic (ROC) curve: practical review for radiologists",
      "volume": "5",
      "author": "Park",
      "year": "2004",
      "journal-title": "Kor. J. Radiol."
    },
    {
      "issue": "3",
      "key": "10.1016/j.commatsci.2018.04.033_b0210",
      "doi-asserted-by": "crossref",
      "first-page": "660",
      "DOI": "10.1021/cm902640j",
      "article-title": "Materials for solid oxide fuel cells",
      "volume": "22",
      "author": "Jacobson",
      "year": "2009",
      "journal-title": "Chem. Mater."
    },
    {
      "issue": "19",
      "key": "10.1016/j.commatsci.2018.04.033_b0215",
      "doi-asserted-by": "crossref",
      "first-page": "5981",
      "DOI": "10.1016/j.actamat.2003.08.004",
      "article-title": "Fuel cell materials and components",
      "volume": "51",
      "author": "Haile",
      "year": "2003",
      "journal-title": "Acta Mater."
    }
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