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Volumn 6, Issue , 2016, Pages

Adaptive Strategies for Materials Design using Uncertainties

Author keywords

[No Author keywords available]

Indexed keywords

PREDICTION; UNCERTAINTY;

EID: 84955620121     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep19660     Document Type: Article
Times cited : (215)

References (35)
  • 1
    • 0033309515 scopus 로고    scopus 로고
    • Neural networks in materials science
    • Bhadeshia, H. K. D. H. Neural Networks in Materials Science. ISIJ International 39, 966-979 (1999).
    • (1999) ISIJ International , vol.39 , pp. 966-979
    • Bhadeshia, H.K.D.H.1
  • 3
    • 84858057930 scopus 로고    scopus 로고
    • Data mining for materials: Computational experiments with AB compounds
    • Saad, Y. Et al. Data mining for materials: Computational experiments with AB compounds. Phys. Rev. B 85, 104104 (2012).
    • (2012) Phys. Rev. B , vol.85 , pp. 104104
    • Saad, Y.1
  • 5
    • 84897608202 scopus 로고    scopus 로고
    • Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single-and binary-component solids
    • Seko, A., Maekawa, T., Tsuda, K. & Tanaka, I. Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single-and binary-component solids. Phys. Rev. B 89, 054303 (2014).
    • (2014) Phys. Rev. B , vol.89 , pp. 054303
    • Seko, A.1    Maekawa, T.2    Tsuda, K.3    Tanaka, I.4
  • 6
    • 84889259535 scopus 로고    scopus 로고
    • Informatics-aided bandgap engineering for solar materials
    • Dey, P. Et al. Informatics-aided bandgap engineering for solar materials. Computational Materials Science 83, 185-195 (2014).
    • (2014) Computational Materials Science , vol.83 , pp. 185-195
    • Dey, P.1
  • 7
    • 84875458397 scopus 로고    scopus 로고
    • The high-throughput highway to computational materials design
    • Curtarolo, S. Et al. The high-throughput highway to computational materials design. Nat Mater. 12, 191-201 (2013).
    • (2013) Nat Mater. , vol.12 , pp. 191-201
    • Curtarolo, S.1
  • 8
    • 84886996545 scopus 로고    scopus 로고
    • Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
    • Jain, A. Et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials 1 (2013).
    • (2013) APL Materials , vol.1
    • Jain, A.1
  • 9
    • 84887236786 scopus 로고    scopus 로고
    • Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
    • Saal, J., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD). JOM 65, 1501-1509 (2013).
    • (2013) JOM , vol.65 , pp. 1501-1509
    • Saal, J.1    Kirklin, S.2    Aykol, M.3    Meredig, B.4    Wolverton, C.5
  • 10
    • 84925727695 scopus 로고    scopus 로고
    • Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds
    • Gautier, R. Et al. Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds. Nat Chem 7, 308-316 (2015).
    • (2015) Nat Chem , vol.7 , pp. 308-316
    • Gautier, R.1
  • 11
    • 0000561424 scopus 로고    scopus 로고
    • Efficient global optimization of expensive black-box functions
    • Jones, D. R., Schonlau, M. & Welch, W. J. Efficient global optimization of expensive black-box functions. J. of Global Optimization 13, 455-492 (1998).
    • (1998) J. of Global Optimization , vol.13 , pp. 455-492
    • Jones, D.R.1    Schonlau, M.2    Welch, W.J.3
  • 12
    • 79961007365 scopus 로고    scopus 로고
    • The illusion of distribution-free small-sample classification in genomics
    • Dougherty, E. R., Zollanvari, A. & Braga-Neto, U. M. The Illusion of Distribution-Free Small-Sample Classification in Genomics. Curr Genomics 12, 333-341 (2011).
    • (2011) Curr Genomics , vol.12 , pp. 333-341
    • Dougherty, E.R.1    Zollanvari, A.2    Braga-Neto, U.M.3
  • 13
    • 84930631638 scopus 로고    scopus 로고
    • Probabilistic machine learning and artificial intelligence
    • Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 521, 452-459 (2015).
    • (2015) Nature , vol.521 , pp. 452-459
    • Ghahramani, Z.1
  • 14
    • 70449498873 scopus 로고    scopus 로고
    • The knowledge-gradient policy for correlated normal beliefs
    • Frazier, P., Powell, W. & Dayanik, S. The knowledge-gradient policy for correlated normal beliefs. INFORMS Journal on Computing 21, 599-613 (2009).
    • (2009) INFORMS Journal on Computing , vol.21 , pp. 599-613
    • Frazier, P.1    Powell, W.2    Dayanik, S.3
  • 17
    • 80555140075 scopus 로고    scopus 로고
    • Scikit-learn: Machine learning in python
    • Pedregosa, F. Et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825-2830 (2011).
    • (2011) Journal of Machine Learning Research , vol.12 , pp. 2825-2830
    • Pedregosa, F.1
  • 19
    • 84905578000 scopus 로고    scopus 로고
    • A genomic approach to the stability, elastic, and electronic properties of the MAX phases
    • Aryal, S., Sakidja, R., Barsoum, M. W. & Ching, W.-Y. A genomic approach to the stability, elastic, and electronic properties of the MAX phases. physica status solidi (b) 251, 1480-1497 (2014).
    • (2014) Physica Status Solidi (B) , vol.251 , pp. 1480-1497
    • Aryal, S.1    Sakidja, R.2    Barsoum, M.W.3    Ching, W.-Y.4
  • 22
    • 39249085484 scopus 로고    scopus 로고
    • Charge-density-shear-moduli relationships in aluminum-lithium alloys
    • Eberhart, M. Charge-Density-Shear-Moduli Relationships in Aluminum-Lithium Alloys. Phys. Rev. Lett. 87, 205503 (2001).
    • (2001) Phys. Rev. Lett. , vol.87 , pp. 205503
    • Eberhart, M.1
  • 23
    • 84860146524 scopus 로고    scopus 로고
    • Extreme Poisson's ratios and their electronic origin in B2 CsCl-type AB intermetallic compounds
    • Wang, X. F., Jones, T. E., Li, W. & Zhou, Y. C. Extreme Poisson's ratios and their electronic origin in B2 CsCl-type AB intermetallic compounds. Phys. Rev. B 85, 134108 (2012).
    • (2012) Phys. Rev. B , vol.85 , pp. 134108
    • Wang, X.F.1    Jones, T.E.2    Li, W.3    Zhou, Y.C.4
  • 24
    • 70349764458 scopus 로고    scopus 로고
    • Influence of the electronic structure on the ductile behavior of B2 CsCl-type AB intermetallics
    • Gschneidner, K. Et al. Influence of the electronic structure on the ductile behavior of B2 CsCl-type AB intermetallics. Acta Materialia 57, 5876-5881 (2009).
    • (2009) Acta Materialia , vol.57 , pp. 5876-5881
    • Gschneidner, K.1
  • 27
    • 84897840142 scopus 로고    scopus 로고
    • Combinatorial screening for new materials in unconstrained composition space with machine learning
    • Meredig, B. Et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B 89, 094104 (2014).
    • (2014) Phys. Rev. B , vol.89 , pp. 094104
    • Meredig, B.1
  • 29
    • 85016437770 scopus 로고    scopus 로고
    • Machine learning in materials science: Recent progress and emerging applications
    • Parrill, A. L. & Lipkowitz, K. B. (eds) Wiley
    • Mueller, T., Kusne, A. G. & Ramprasad, R. Machine learning in materials science: Recent progress and emerging applications. In Parrill, A. L. & Lipkowitz, K. B. (eds) Reviews in Computational Chemistry vol. 29 (Wiley, 2016).
    • (2016) Reviews in Computational Chemistry , vol.29
    • Mueller, T.1    Kusne, A.G.2    Ramprasad, R.3
  • 30
    • 84947783068 scopus 로고    scopus 로고
    • Materials Informatics: The Materials "Gene" and Big Data
    • Rajan, K. Materials Informatics: The Materials "Gene" and Big Data. Annual Review of Materials Research 45, 153-169 (2015).
    • (2015) Annual Review of Materials Research , vol.45 , pp. 153-169
    • Rajan, K.1
  • 32
    • 84932619170 scopus 로고    scopus 로고
    • A predictive machine learning approach for microstructure optimization and materials design
    • Liu, R. Et al. A predictive machine learning approach for microstructure optimization and materials design. Scientific Reports 5, 11551 (2015).
    • (2015) Scientific Reports , vol.5 , pp. 11551
    • Liu, R.1
  • 33
    • 84942306511 scopus 로고    scopus 로고
    • Big-deep-smart data in imaging for guiding materials design
    • Kalinin, S. V., Sumpter, B. G. & Archibald, R. K. Big-deep-smart data in imaging for guiding materials design. Nat Mater 14, 973-980 (2015).
    • (2015) Nat Mater , vol.14 , pp. 973-980
    • Kalinin, S.V.1    Sumpter, B.G.2    Archibald, R.K.3
  • 34
    • 33746931581 scopus 로고    scopus 로고
    • On outliers and activity cliffs: Why QSAR often disappoints
    • Maggiora, G. M. On Outliers and Activity Cliffs: Why QSAR Often Disappoints. Journal of Chemical Information and Modeling 46, 1535-1535 (2006).
    • (2006) Journal of Chemical Information and Modeling , vol.46 , pp. 1535
    • Maggiora, G.M.1
  • 35
    • 57849156863 scopus 로고    scopus 로고
    • Voyages to the (un)known: Adaptive design of bioactive compounds
    • Schneider, G. Et al. Voyages to the (un)known: Adaptive design of bioactive compounds. Trends in Biotechnology 27, 18-26 (2009).
    • (2009) Trends in Biotechnology , vol.27 , pp. 18-26
    • Schneider, G.1


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