skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Combinatorial screening for new materials in unconstrained composition space with machine learning

Authors:
; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Revolutionary Materials for Solid State Energy Conversion (RMSSEC)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1383662
DOE Contract Number:  
SC0001054
Resource Type:
Journal Article
Journal Name:
Physical Review. B, Condensed Matter and Materials Physics
Additional Journal Information:
Journal Volume: 89; Journal Issue: 9; Related Information: RMSSEC partners with Michigan State University (lead); University of California, Los Angeles; University of Michigan; Northwestern University; Oak Ridge National Laboratory; Ohio State University; Wayne State University; Journal ID: ISSN 1098-0121
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
solar (thermal), phonons, thermal conductivity, thermoelectric, mechanical behavior, charge transport, materials and chemistry by design, synthesis (novel materials), synthesis (self-assembly), synthesis (scalable processing)

Citation Formats

Meredig, B., Agrawal, A., Kirklin, S., Saal, J. E., Doak, J. W., Thompson, A., Zhang, K., Choudhary, A., and Wolverton, C. Combinatorial screening for new materials in unconstrained composition space with machine learning. United States: N. p., 2014. Web. doi:10.1103/PhysRevB.89.094104.
Meredig, B., Agrawal, A., Kirklin, S., Saal, J. E., Doak, J. W., Thompson, A., Zhang, K., Choudhary, A., & Wolverton, C. Combinatorial screening for new materials in unconstrained composition space with machine learning. United States. doi:10.1103/PhysRevB.89.094104.
Meredig, B., Agrawal, A., Kirklin, S., Saal, J. E., Doak, J. W., Thompson, A., Zhang, K., Choudhary, A., and Wolverton, C. Sat . "Combinatorial screening for new materials in unconstrained composition space with machine learning". United States. doi:10.1103/PhysRevB.89.094104.
@article{osti_1383662,
title = {Combinatorial screening for new materials in unconstrained composition space with machine learning},
author = {Meredig, B. and Agrawal, A. and Kirklin, S. and Saal, J. E. and Doak, J. W. and Thompson, A. and Zhang, K. and Choudhary, A. and Wolverton, C.},
abstractNote = {},
doi = {10.1103/PhysRevB.89.094104},
journal = {Physical Review. B, Condensed Matter and Materials Physics},
issn = {1098-0121},
number = 9,
volume = 89,
place = {United States},
year = {2014},
month = {3}
}

Works referenced in this record:

Half-Heusler Semiconductors as Piezoelectrics
journal, July 2012


Bayesian approach to cluster expansions
journal, July 2009


Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
journal, January 2012


Hydrogen storage in calcium alanate: First-principles thermodynamics and crystal structures
journal, February 2007


Finding Density Functionals with Machine Learning
journal, June 2012


A search model for topological insulators with high-throughput robustness descriptors
journal, May 2012

  • Yang, Kesong; Setyawan, Wahyu; Wang, Shidong
  • Nature Materials, Vol. 11, Issue 7
  • DOI: 10.1038/nmat3332

Hexagonal A B C Semiconductors as Ferroelectrics
journal, October 2012


Detecting Novel Associations in Large Data Sets
journal, December 2011


Measuring the accuracy of diagnostic systems
journal, June 1988


High-Throughput Computational Screening of New Li-Ion Battery Anode Materials
journal, September 2012

  • Kirklin, Scott; Meredig, Bryce; Wolverton, Chris
  • Advanced Energy Materials, Vol. 3, Issue 2
  • DOI: 10.1002/aenm.201200593

Identification of Potential Photovoltaic Absorbers Based on First-Principles Spectroscopic Screening of Materials
journal, February 2012


Inhomogeneous Electron Gas
journal, November 1964


Ionic high-pressure form of elemental boron
journal, January 2009

  • Oganov, Artem R.; Chen, Jiuhua; Gatti, Carlo
  • Nature, Vol. 457, Issue 7231
  • DOI: 10.1038/nature07736

Global space-group optimization problem: Finding the stablest crystal structure without constraints
journal, March 2007


A high-throughput infrastructure for density functional theory calculations
journal, June 2011


Sorting Stable versus Unstable Hypothetical Compounds: The Case of Multi-Functional ABX Half-Heusler Filled Tetrahedral Structures
journal, February 2012

  • Zhang, Xiuwen; Yu, Liping; Zakutayev, Andriy
  • Advanced Functional Materials, Vol. 22, Issue 7
  • DOI: 10.1002/adfm.201102546

Predicting Crystal Structures with Data Mining of Quantum Calculations
journal, September 2003


Correcting density functional theory for accurate predictions of compound enthalpies of formation: Fitted elemental-phase reference energies
journal, March 2012


USPEX—Evolutionary crystal structure prediction
journal, December 2006

  • Glass, Colin W.; Oganov, Artem R.; Hansen, Nikolaus
  • Computer Physics Communications, Vol. 175, Issue 11-12
  • DOI: 10.1016/j.cpc.2006.07.020

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
journal, June 2010

  • Hautier, Geoffroy; Fischer, Christopher C.; Jain, Anubhav
  • Chemistry of Materials, Vol. 22, Issue 12
  • DOI: 10.1021/cm100795d

Materials informatics
journal, October 2005


Transparent dense sodium
journal, March 2009

  • Ma, Yanming; Eremets, Mikhail; Oganov, Artem R.
  • Nature, Vol. 458, Issue 7235
  • DOI: 10.1038/nature07786

COMPUTER SCIENCE: Beyond the Data Deluge
journal, March 2009


Predicting crystal structure by merging data mining with quantum mechanics
journal, July 2006

  • Fischer, Christopher C.; Tibbetts, Kevin J.; Morgan, Dane
  • Nature Materials, Vol. 5, Issue 8
  • DOI: 10.1038/nmat1691

Self-Consistent Equations Including Exchange and Correlation Effects
journal, November 1965


Predicting stable stoichiometries of compounds via evolutionary global space-group optimization
journal, September 2009