Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System

Клименко, Д. Н.; Stepanov, Nikita; Li, Jia; Fang, Qihong; Zherebtsov, Sergey

doi:10.3390/ma14237213

Cited by 20 publications

(8 citation statements)

References 71 publications

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“…Machine learning, a data-driven approach, has been employed to predict the properties of HEAs as well as several other alloys. Furthermore, material researchers have found it can overcome the limitations of the above-mentioned approaches [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] . Zhang et al 18 , Wen et al 27,30 , Zheng et al 29 , Klimenko et al 32 , Guo et al 23 , and Li et al 26 developed machine learning models which predict the mechanical properties of the HEAs.…”

Section: Introductionmentioning

confidence: 99%

A neural network model for high entropy alloy design

et al. 2023

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A neural network model is developed to search vast compositional space of high entropy alloys (HEAs). The model predicts the mechanical properties of HEAs better than several other models. It’s because the special structure of the model helps the model understand the characteristics of constituent elements of HEAs. In addition, thermodynamics descriptors were utilized as input to the model so that the model predicts better by understanding the thermodynamic properties of HEAs. A conditional random search, which is good at finding local optimal values, was selected as the inverse predictor and designed two HEAs using the model. We experimentally verified that the HEAs have the best combination of strength and ductility and this proves the validity of the model and alloy design method. The strengthening mechanism of the designed HEAs is further discussed based on microstructure and lattice distortion effect. The present alloy design approach, specialized in finding multiple local optima, could help researchers design an infinite number of new alloys with interesting properties.

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Section: Introductionmentioning

confidence: 99%

A neural network model for high entropy alloy design

et al. 2023

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“…Apart from such an algorithm, numerous techniques, i.e., the gradient boosting model, trained with 1,807 datasets, demonstrated high accuracy of 96.41% for predicting single-phase and non-single-phase refractory HEAs (RHEAs) 22 . Other methods also exhibit high accuracy prediction, e.g., a combined ML and CALPHAD technique, an artificial neural network technique (ANN) coupled with experimental data 9 , 10 , 12 , 23 – 28 . In addition to phase formation of HEAs, machine learning was recently employed to predict the mechanical properties of HEA bulk materials, including microhardness 10 , 27 , yield strength 12 , 23 , dislocation density 12 , elastic modulus 29 , Young’s modulus 30 , hardness 11 , 31 , and elastic constant 32 .…”

Section: Introductionmentioning

confidence: 99%

“…Other methods also exhibit high accuracy prediction, e.g., a combined ML and CALPHAD technique, an artificial neural network technique (ANN) coupled with experimental data 9 , 10 , 12 , 23 – 28 . In addition to phase formation of HEAs, machine learning was recently employed to predict the mechanical properties of HEA bulk materials, including microhardness 10 , 27 , yield strength 12 , 23 , dislocation density 12 , elastic modulus 29 , Young’s modulus 30 , hardness 11 , 31 , and elastic constant 32 . These shed light on the ML-based high-throughput screening of HEA materials.…”

Section: Introductionmentioning

confidence: 99%

High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

Rittiruam

Noppakhun

Setasuban

et al. 2022

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This work introduced the high-throughput phase prediction of PtPd-based high-entropy alloys via the algorithm based on a combined Korringa-Kohn-Rostoker coherent potential approximation (KKR-CPA) and artificial neural network (ANN) technique. As the first step, the KKR-CPA was employed to generate 2,720 data of formation energy and lattice parameters in the framework of the first-principles density functional theory. Following the data generation, 15 features were selected and verified for all HEA systems in each phase (FCC and BCC) via ANN. The algorithm exhibited high accuracy for all four prediction models on 36,556 data from 9139 HEA systems with 137,085 features, verified by R2 closed to unity and the mean relative error (MRE) within 5%. From this dataset comprising 5002 and 4137 systems of FCC and BCC phases, it can be realized based on the highest tendency of HEA phase formation that (1) Sc, Co, Cu, Zn, Y, Ru, Cd, Os, Ir, Hg, Al, Si, P, As, and Tl favor FCC phase, (2) Hf, Ga, In, Sn, Pb, and Bi favor BCC phase, and (3) Ti, V, Cr, Mn, Fe, Ni, Zr, Nb, Mo, Tc, Rh, Ag, Ta, W, Re, Au, Ge, and Sb can be found in both FCC and BCC phases with comparable tendency, where all predictions are in good agreement with the data from the literature. Thus, the combination of KKR-CPA and ANN can reduce the computational cost for the screening of PtPd-based HEA and accurately predict the structure, i.e., FCC, BCC, etc.

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“…Meanwhile, they also experimentally synthesized the alloy to validate their hardness prediction. Klimenko et al [ 33 ] predicted the strength of AlCrNbTiVZr RHEA using a support vector ML model with experimental validation. Using a random forest model, the Kwak group [ 34 ] predicted various mechanical properties, such as tensile strength, nanoindentation hardness, and elongation of γ- TiAl alloys.…”

Section: Introductionmentioning

confidence: 99%

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach

et al. 2022

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Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.

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Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System

Cited by 20 publications

References 71 publications

A neural network model for high entropy alloy design

A neural network model for high entropy alloy design

High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach

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