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Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization.

Yingying Ma1, Minjie Li2, Yongkun Mu3

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Journal of Chemical Information and Modeling
|September 26, 2023
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Summary
This summary is machine-generated.

Machine learning accelerates the design of high-entropy alloys (HEAs) for wear resistance. This study developed a framework to predict and optimize HEA hardness and ductility, identifying promising new alloy compositions.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Alloy Design

Background:

  • High-entropy alloys (HEAs) offer potential for wear-resistant applications due to their high hardness and ductility.
  • Traditional alloy design methods struggle with the vast compositional space of HEAs, hindering the discovery of novel materials with multiple desired properties.

Purpose of the Study:

  • To develop a machine learning (ML)-based framework for designing high-entropy alloys (HEAs) with enhanced Vickers hardness (H) and compressive fracture strain (D).
  • To identify optimal alloying compositions through virtual screening and genetic algorithms.

Main Methods:

  • Construction of a large dataset (172,467 data points) with 161 features for predicting H and D.
  • Feature selection using Support Vector Regression (SVR) and Light Gradient Boosting Machine (LightGBM) algorithms, identifying 12 features for D and 8 for H.
  • Application of the Nondominated Sorting Genetic Algorithm II (NSGA-II) and virtual screening to discover optimal HEA compositions.

Main Results:

  • Machine learning models achieved high predictive accuracy, with R-squared values of 0.76 for D and 0.90 for H during 10-fold cross-validation.
  • Four novel HEA candidates were synthesized and validated, with three showing significant improvements in ductility (135.8%, 282.4%, 194.1%) at comparable hardness levels.
  • Recommended elemental composition ranges for Al, Nb, and Mo to achieve high hardness and ductility in HEAs.

Conclusions:

  • The proposed ML framework effectively accelerates the design and discovery of high-entropy alloys with superior hardness and ductility.
  • The validated HEA candidates and recommended composition ranges provide valuable guidance for developing advanced wear-resistant materials.