Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

Furmanchuk, Al'ona; Saal, James E.; Doak, Jeff W.; Olson, Gregory B.; Choudhary, Alok; Agrawal, Ankit

doi:10.1002/jcc.25067

Title: Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

Abstract

The regression model‐based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off‐stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application ( http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor ) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc.

Authors:

^[1]; Saal, James E. ^[2]; Doak, Jeff W. ^[2]; Olson, Gregory B. ^[2]; Choudhary, Alok ^[1]; Agrawal, Ankit ^[1]

Northwestern University, Evanston, IL (United States)
QuesTeck Innovations LLC, Evanston, IL (United States)

Publication Date:: Wed Sep 27 00:00:00 EDT 2017

Research Org.:: Northwestern Univ., Evanston, IL (United States)

Sponsoring Org.:: USDOE Office of Science (SC); National Institute of Standards and Technology (NIST); Defense Advanced Research Project Agency (DARPA); SPAWAR Systems Center Pacific (SSC Pacific); US Air Force Office of Scientific Research (AFOSR); National Science Foundation (NSF)

OSTI Identifier:: 1537575

Alternate Identifier(s):: OSTI ID: 1395185

Grant/Contract Number:: SC0007456; SC0014330; N66001-15-C-4036; 70NANB14H012; FA9550-12-1-0458; CCF-1409601; DE‐SC0007456, DE‐SC0014330

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Computational Chemistry

Additional Journal Information:: Journal Volume: 39; Journal Issue: 4; Journal ID: ISSN 0192-8651

Publisher:: Wiley

Country of Publication:: United States

Language:: English

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats


                    Furmanchuk, Al'ona, Saal, James E., Doak, Jeff W., Olson, Gregory B., Choudhary, Alok, and Agrawal, Ankit. Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach.  United States: N. p., 2017. 
Web.  doi:10.1002/jcc.25067.

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                    Furmanchuk, Al'ona, Saal, James E., Doak, Jeff W., Olson, Gregory B., Choudhary, Alok, & Agrawal, Ankit. Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach.  United States.  https://doi.org/10.1002/jcc.25067

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                    Furmanchuk, Al'ona, Saal, James E., Doak, Jeff W., Olson, Gregory B., Choudhary, Alok, and Agrawal, Ankit. Wed .  
"Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach".  United States.  https://doi.org/10.1002/jcc.25067.  https://www.osti.gov/servlets/purl/1537575.

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@article{osti_1537575,

  title        = {Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach},

  author       = {Furmanchuk, Al'ona and Saal, James E. and Doak, Jeff W. and Olson, Gregory B. and Choudhary, Alok and Agrawal, Ankit},

  abstractNote = {The regression model‐based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off‐stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application ( http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor ) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc.},

  doi          = {10.1002/jcc.25067},

  journal      = {Journal of Computational Chemistry},

  number       = 4,

  volume       = 39,

  place        = {United States},

  year         = {Wed Sep 27 00:00:00 EDT 2017},

  month        = {Wed Sep 27 00:00:00 EDT 2017}

}

Copy to clipboard

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Title: Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

Abstract

Citation Formats

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science journal, April 2016

Effective ionic radii in oxides and fluorides journal, May 1969

Overview of Solid-State Thermoelectric Refrigerators and Possible Applications to On-Chip Thermal Management journal, August 2006

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A predictive machine learning approach for microstructure optimization and materials design journal, June 2015

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Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides journal, September 1976

Atomic Screening Constants from SCF Functions journal, June 1963

Enhanced Thermoelectric Properties in Zinc Antimonides journal, September 2011

Doping-dependent thermopower of PbTe from Boltzmann transport calculations journal, May 2010

Defect engineering for a markedly increased electrical conductivity and power factor in doped ZnO ceramic journal, October 2016

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Effects of Sc and Y substitution on the structure and thermoelectric properties of Yb14MnSb11 journal, October 2016

Thermoelectric properties of polycrystalline La1−xSrxCoO3 journal, November 2008

Materials for thermoelectric energy conversion journal, April 1988

A survey of cross-validation procedures for model selection journal, January 2010

Thermoelectric properties of perovskites: Sign change of the Seebeck coefficient and high temperature properties journal, January 2007

Thermoelectric properties of the Bi2−xYxRu2O7 (x=0–2) pyrochlores journal, April 2003

Atomic Radii in Crystals journal, November 1964

Enhancement of thermoelectric performance in Cd-doped Ca 3 Co 4 O 9 via spin entropy, defect chemistry and phonon scattering journal, January 2014

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Instance-based learning algorithms journal, January 1991

The random subspace method for constructing decision forests journal, January 1998

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
journal, April 2016

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journal, May 1969

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journal, August 2006

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journal, December 2015

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journal, June 2015

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journal, May 2013

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journal, September 1976

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journal, June 1963

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journal, September 2011

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journal, May 2010

Defect engineering for a markedly increased electrical conductivity and power factor in doped ZnO ceramic
journal, October 2016

Electronic and thermoelectric properties of InN studied using ab initio density functional theory and Boltzmann transport calculations
journal, December 2014

Effects of Sc and Y substitution on the structure and thermoelectric properties of Yb14MnSb11
journal, October 2016

Thermoelectric properties of polycrystalline La1−xSrxCoO3
journal, November 2008

Materials for thermoelectric energy conversion
journal, April 1988

A survey of cross-validation procedures for model selection
journal, January 2010

Thermoelectric properties of perovskites: Sign change of the Seebeck coefficient and high temperature properties
journal, January 2007

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journal, April 2003

Atomic Radii in Crystals
journal, November 1964

Enhancement of thermoelectric performance in Cd-doped Ca ₃ Co ₄ O ₉ via spin entropy, defect chemistry and phonon scattering
journal, January 2014

Coulomb interaction and spin-orbit coupling calculations of thermoelectric properties of the quaternary chalcogenides Tl ₂ PbXY ₄ (X = Zr, Hf and Y = S, Se)
journal, September 2015

Multivariate Method-Assisted Ab Initio Study of Olivine-Type LiMXO ₄ (Main Group M ²⁺ –X ⁵⁺ and M ³⁺ –X ⁴⁺ ) Compositions as Potential Solid Electrolytes
journal, March 2012

A semiempirical approach to the prediction of compound formation for 3486 binary alloy systems
journal, July 1985

Printed flexible thermoelectric generators for use on low levels of waste heat
journal, October 2015

Optoelectronic structure and related transport properties of BiCuSeO-based oxychalcogenides: First principle calculations
journal, August 2016

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journal, January 2011

Evaluation of Half-Heusler Compounds as Thermoelectric Materials Based on the Calculated Electrical Transport Properties
journal, October 2008

Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties
journal, May 2016

Electrical Transport Properties and High-Temperature Thermoelectric Performance of (Ca0.9M0.1)MnO3 (M = Y, La, Ce, Sm, In, Sn, Sb, Pb, Bi)
journal, November 1995

Atomic-environment classification of the chemical elements
journal, June 1993

Performance review of a novel combined thermoelectric power generation and water desalination system
journal, November 2015

Ab initio study of thermoelectric properties of doped SnO2 superlattices
journal, November 2015

Geometric effect on cooling power and performance of an integrated thermoelectric generation-cooling system
journal, November 2014

Half-metallic properties, single-spin negative differential resistance, and large single-spin Seebeck effects induced by chemical doping in zigzag-edged graphene nanoribbons
journal, January 2015

Universal fragment descriptors for predicting properties of inorganic crystals
journal, June 2017

Recent Developments in Semiconductor Thermoelectric Physics and Materials
journal, August 2011

Thermoelectric properties of the unfilled skutterudite ${FeSb}_{3}$ from first principles and Seebeck local probes
journal, November 2015

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids
journal, February 2014

Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints
journal, January 2015

Ab initio optimization of phonon drag effect for lower-temperature thermoelectric energy conversion
journal, November 2015

Predictive analytics for crystalline materials: bulk modulus
journal, January 2016

Combinatorial screening for new materials in unconstrained composition space with machine learning
journal, March 2014

First-principles study of electronic, thermoelectric and thermal properties of Sb2S3
journal, April 2016

First-principles investigation of structural, electronic, and thermoelectric properties of n - and p -type Mg ₂ Si
journal, August 2015

Data mining our way to the next generation of thermoelectrics
journal, January 2016

Instance-based learning algorithms
journal, January 1991

The random subspace method for constructing decision forests
journal, January 1998