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Discovery of high-entropy ceramics via machine learning

Publication ,  Journal Article
Kaufmann, K; Maryanovsky, D; Mellor, WM; Zhu, C; Rosengarten, AS; Harrington, TJ; Oses, C; Toher, C; Curtarolo, S; Vecchio, KS
Published in: npj Computational Materials
December 1, 2020

Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance.

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Published In

npj Computational Materials

DOI

EISSN

2057-3960

Publication Date

December 1, 2020

Volume

6

Issue

1

Related Subject Headings

  • 5104 Condensed matter physics
  • 4016 Materials engineering
  • 3407 Theoretical and computational chemistry
 

Citation

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Kaufmann, K., Maryanovsky, D., Mellor, W. M., Zhu, C., Rosengarten, A. S., Harrington, T. J., … Vecchio, K. S. (2020). Discovery of high-entropy ceramics via machine learning. Npj Computational Materials, 6(1). https://doi.org/10.1038/s41524-020-0317-6
Kaufmann, K., D. Maryanovsky, W. M. Mellor, C. Zhu, A. S. Rosengarten, T. J. Harrington, C. Oses, C. Toher, S. Curtarolo, and K. S. Vecchio. “Discovery of high-entropy ceramics via machine learning.” Npj Computational Materials 6, no. 1 (December 1, 2020). https://doi.org/10.1038/s41524-020-0317-6.
Kaufmann K, Maryanovsky D, Mellor WM, Zhu C, Rosengarten AS, Harrington TJ, et al. Discovery of high-entropy ceramics via machine learning. npj Computational Materials. 2020 Dec 1;6(1).
Kaufmann, K., et al. “Discovery of high-entropy ceramics via machine learning.” Npj Computational Materials, vol. 6, no. 1, Dec. 2020. Scopus, doi:10.1038/s41524-020-0317-6.
Kaufmann K, Maryanovsky D, Mellor WM, Zhu C, Rosengarten AS, Harrington TJ, Oses C, Toher C, Curtarolo S, Vecchio KS. Discovery of high-entropy ceramics via machine learning. npj Computational Materials. 2020 Dec 1;6(1).

Published In

npj Computational Materials

DOI

EISSN

2057-3960

Publication Date

December 1, 2020

Volume

6

Issue

1

Related Subject Headings

  • 5104 Condensed matter physics
  • 4016 Materials engineering
  • 3407 Theoretical and computational chemistry