Yield strength prediction of high-entropy alloys using machine learning
Abstract
Yield strength at high temperature is an important parameter in the design and application of high entropy alloys (HEAs). However, the experimental measurement of yield strength at high temperature is quite costly, complicated, and time-consuming. Therefore, it is essential to identify and apply a robust method for the accurate prediction of yield strength at high temperature from the available experimental and simulation data. In this study, for the first time, a machine learning (ML) method based on the regression technique of random forest (RF) regressor is used to predict the yield strength of HEAs at the desired temperature. Further, the yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 °C and 1200 °C, are predicted using the RF regressor model. We find that the results are consistent with the experimental reports, showing that the RF regressor model predicts the yield strength of HEAs at the desired temperatures with high accuracy.
- Publication Date:
- Research Org.:
- Southern Univ. and A&M College, Baton Rouge, LA (United States)
- Sponsoring Org.:
- USDOE; USDOD
- OSTI Identifier:
- 1865395
- Alternate Identifier(s):
- OSTI ID: 1781237
- Grant/Contract Number:
- NA0003979; 1541079; LEQSF-EPS(2020)-SURE-237; LEQSF-EPS(2020)-SURE-242; W911NF1910005
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Materials Today Communications
- Additional Journal Information:
- Journal Volume: 26; Journal ID: ISSN 2352-4928
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; High entropy alloys; Random forest model; Yield strength prediction; MoNbTaTiW; HfMoNbTaTiZr
Citation Formats
Bhandari, Uttam, Rafi, Md. Rumman, Zhang, Congyan, and Yang, Shizhong. Yield strength prediction of high-entropy alloys using machine learning. United States: N. p., 2020.
Web. doi:10.1016/j.mtcomm.2020.101871.
Bhandari, Uttam, Rafi, Md. Rumman, Zhang, Congyan, & Yang, Shizhong. Yield strength prediction of high-entropy alloys using machine learning. United States. https://doi.org/10.1016/j.mtcomm.2020.101871
Bhandari, Uttam, Rafi, Md. Rumman, Zhang, Congyan, and Yang, Shizhong. Fri .
"Yield strength prediction of high-entropy alloys using machine learning". United States. https://doi.org/10.1016/j.mtcomm.2020.101871. https://www.osti.gov/servlets/purl/1865395.
@article{osti_1865395,
title = {Yield strength prediction of high-entropy alloys using machine learning},
author = {Bhandari, Uttam and Rafi, Md. Rumman and Zhang, Congyan and Yang, Shizhong},
abstractNote = {Yield strength at high temperature is an important parameter in the design and application of high entropy alloys (HEAs). However, the experimental measurement of yield strength at high temperature is quite costly, complicated, and time-consuming. Therefore, it is essential to identify and apply a robust method for the accurate prediction of yield strength at high temperature from the available experimental and simulation data. In this study, for the first time, a machine learning (ML) method based on the regression technique of random forest (RF) regressor is used to predict the yield strength of HEAs at the desired temperature. Further, the yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 °C and 1200 °C, are predicted using the RF regressor model. We find that the results are consistent with the experimental reports, showing that the RF regressor model predicts the yield strength of HEAs at the desired temperatures with high accuracy.},
doi = {10.1016/j.mtcomm.2020.101871},
journal = {Materials Today Communications},
number = ,
volume = 26,
place = {United States},
year = {Fri Nov 13 00:00:00 EST 2020},
month = {Fri Nov 13 00:00:00 EST 2020}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
Microstructure and mechanical properties at elevated temperatures of a new Al-containing refractory high-entropy alloy Nb-Mo-Cr-Ti-Al
journal, March 2016
- Chen, H.; Kauffmann, A.; Gorr, B.
- Journal of Alloys and Compounds, Vol. 661
Theory of screw dislocation strengthening in random BCC alloys from dilute to “High-Entropy” alloys
journal, January 2020
- Maresca, Francesco; Curtin, William A.
- Acta Materialia, Vol. 182
The evolution of the deformation substructure in a Ni-Co-Cr equiatomic solid solution alloy
journal, June 2017
- Miao, J.; Slone, C. E.; Smith, T. M.
- Acta Materialia, Vol. 132
Microstructure and Properties of Aluminum-Containing Refractory High-Entropy Alloys
journal, July 2014
- Senkov, O. N.; Woodward, C.; Miracle, D. B.
- JOM, Vol. 66, Issue 10
Random Forests and Adaptive Nearest Neighbors
journal, June 2006
- Lin, Yi; Jeon, Yongho
- Journal of the American Statistical Association, Vol. 101, Issue 474
Prediction of the Composition and Hardness of High-Entropy Alloys by Machine Learning
journal, July 2019
- Chang, Yao-Jen; Jui, Chia-Yung; Lee, Wen-Jay
- JOM, Vol. 71, Issue 10
Machine learning for phase selection in multi-principal element alloys
journal, July 2018
- Islam, Nusrat; Huang, Wenjiang; Zhuang, Houlong L.
- Computational Materials Science, Vol. 150
Effect of Ti additions on mechanical properties of NbMoTaW and VNbMoTaW refractory high entropy alloys
journal, May 2017
- Han, Z. D.; Chen, N.; Zhao, S. F.
- Intermetallics, Vol. 84
Predicting yield strengths of noble metal high entropy alloys
journal, January 2018
- Varvenne, Céline; Curtin, William A.
- Scripta Materialia, Vol. 142
Phase evolution and mechanical properties of novel FeCoNiCuMox high entropy alloys
journal, April 2020
- Soni, Vinay Kumar; Sanyal, Shubhashis; Sinha, Sudip K.
- Vacuum, Vol. 174
Enhanced mechanical properties of HfMoTaTiZr and HfMoNbTaTiZr refractory high-entropy alloys
journal, July 2015
- Juan, Chien-Chang; Tsai, Ming-Hung; Tsai, Che-Wei
- Intermetallics, Vol. 62
First-principles-based prediction of yield strength in the RhIrPdPtNiCu high-entropy alloy
journal, February 2019
- Yin, Binglun; Curtin, William A.
- npj Computational Materials, Vol. 5, Issue 1
Mechanical properties of low-density, refractory multi-principal element alloys of the Cr–Nb–Ti–V–Zr system
journal, March 2013
- Senkov, O. N.; Senkova, S. V.; Miracle, D. B.
- Materials Science and Engineering: A, Vol. 565
Microstructure and mechanical properties of refractory HfMo0.5NbTiV0.5Six high-entropy composites
journal, February 2017
- Liu, Yuan; Zhang, Yan; Zhang, Heng
- Journal of Alloys and Compounds, Vol. 694
Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models
journal, February 2020
- Zhang, Yan; Wen, Cheng; Wang, Changxin
- Acta Materialia, Vol. 185
Nanostructured High-Entropy Alloys with Multiple Principal Elements: Novel Alloy Design Concepts and Outcomes
journal, May 2004
- Yeh, J.-W.; Chen, S.-K.; Lin, S.-J.
- Advanced Engineering Materials, Vol. 6, Issue 5, p. 299-303
Computational and experimental investigation of refractory high entropy alloy Mo15Nb20Re15Ta30W20
journal, July 2020
- Bhandari, Uttam; Zhang, Congyan; Zeng, Congyuan
- Journal of Materials Research and Technology, Vol. 9, Issue 4
Solid-Solution Phase Formation Rules for Multi-component Alloys
journal, June 2008
- Zhang, Y.; Zhou, Y. J.; Lin, J. P.
- Advanced Engineering Materials, Vol. 10, Issue 6, p. 534-538
Artificial Intelligence Predicts Body-Centered-Cubic and Face-Centered-Cubic Phases in High-Entropy Alloys
journal, August 2019
- Agarwal, Abhishek; Prasada Rao, A. K.
- JOM, Vol. 71, Issue 10
Mechanical and Thermal Properties of Low-Density Al20+xCr20-xMo20-yTi20V20+y Alloys
journal, April 2020
- Bhandari, Uttam; Zhang, Congyan; Yang, Shizhong
- Crystals, Vol. 10, Issue 4
Mechanical properties of refractory high-entropy alloys: Experiments and modeling
journal, March 2017
- Yao, H. W.; Qiao, J. W.; Hawk, J. A.
- Journal of Alloys and Compounds, Vol. 696
Microstructure and elevated temperature properties of a refractory TaNbHfZrTi alloy
journal, January 2012
- Senkov, O. N.; Scott, J. M.; Senkova, S. V.
- Journal of Materials Science, Vol. 47, Issue 9
Strong and Ductile Non-equiatomic High-Entropy Alloys: Design, Processing, Microstructure, and Mechanical Properties
journal, August 2017
- Li, Zhiming; Raabe, Dierk
- JOM, Vol. 69, Issue 11
Machine learning guided appraisal and exploration of phase design for high entropy alloys
journal, December 2019
- Zhou, Ziqing; Zhou, Yeju; He, Quanfeng
- npj Computational Materials, Vol. 5, Issue 1
Structure and mechanical properties of the AlCrxNbTiV (x = 0, 0.5, 1, 1.5) high entropy alloys
journal, December 2015
- Stepanov, N. D.; Yurchenko, N. Yu; Skibin, D. V.
- Journal of Alloys and Compounds, Vol. 652
Effect of aluminum on the microstructure and properties of two refractory high-entropy alloys
journal, April 2014
- Senkov, O. N.; Senkova, S. V.; Woodward, C.
- Acta Materialia, Vol. 68
Effect of valence electron concentration on stability of fcc or bcc phase in high entropy alloys
journal, May 2011
- Guo, Sheng; Ng, Chun; Lu, Jian
- Journal of Applied Physics, Vol. 109, Issue 10
A fracture-resistant high-entropy alloy for cryogenic applications
journal, September 2014
- Gludovatz, B.; Hohenwarter, A.; Catoor, D.
- Science, Vol. 345, Issue 6201
Machine learning assisted design of high entropy alloys with desired property
journal, May 2019
- Wen, Cheng; Zhang, Yan; Wang, Changxin
- Acta Materialia, Vol. 170
Mechanical properties of high-entropy alloys with emphasis on face-centered cubic alloys
journal, May 2019
- Li, Zezhou; Zhao, Shiteng; Ritchie, Robert O.
- Progress in Materials Science, Vol. 102
First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation
journal, December 2019
- Kim, George; Diao, Haoyan; Lee, Chanho
- Acta Materialia, Vol. 181
Machine-learning phase prediction of high-entropy alloys
journal, May 2019
- Huang, Wenjiang; Martin, Pedro; Zhuang, Houlong L.
- Acta Materialia, Vol. 169
Comprehensive data compilation on the mechanical properties of refractory high-entropy alloys
journal, December 2018
- Couzinié, J. -P.; Senkov, O. N.; Miracle, D. B.
- Data in Brief, Vol. 21