Accelerated design of eutectic high-entropy alloys using high-throughput phase diagram calculations and machine learning

• A high-throughput phase diagram calculations plus machine learning approach was proposed to accelerate the design of eutectic-like dual-phase HEAs in a VNbTiSi-based system. • The design of experiments method was used to verify the eutectic-like duplex microstructure and to narrow down the data screening conditions. • A machine learning classification model that consists of eutectic, near-eutectic and non-eutectic compositions was established according to the eutectic-forming ability. • The proposed approach was evidenced in both five- and six-elemental multi-component alloys and expected to be extended to other alloy systems. Designing eutectic high-entropy alloys (HEAs) with a stable dual-phase structure is prospective for high-temperature structural materials, but remains a challenge due to the complexity of multicomponent compositional space. This study introduces a machine learning (ML) based classification model to assist the high-throughput thermodynamic calculations (HTCs), improving the efficiency by identifying eutectic, near-eutectic and non-eutectic compositions in the VNbTiTaSi system using the parameters that determine the eutectic forming ability. These parameters include the volume fraction of silicides, the liquidus temperature, the eutectic reaction temperature, the melting point, and the temperature interval, obtained from HTCs. The design of experiments (DOE) method was applied to verify the eutectic-like duplex microstructure and to narrow down the range of the abovementioned parameters. Ultimately, three eutectic alloys, V 36 Nb 31 Ti 13 Ta 5 Si 15 , V 32 Nb 29 Ti 17 Mo 7 Si 15 , and V 32 Nb 28 Ti 13 Ta 5 Mo 7 Si 15 , were selected from the eutectic zone identified by the ML models and demonstrated exceptional compression properties at elevated temperatures. This ML combining HTCs eutectic design method is expected to be extendable to multi-component alloys with even more elements and other alloy systems.

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Publication Details

Journal
Materials & Design
Published
2025-05-20
DOI
https://doi.org/10.1016/j.matdes.2025.114125
Citations
5
Primary Topic
High Entropy Alloys Studies
Type
article
Field-Weighted Citation Impact
3.57

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article

Accelerated design of eutectic high-entropy alloys using high-throughput phase diagram calculations and machine learning

J.X. Wang, Binggang Guan, Mingxu Wu, Da Shu et al.
5 citations
Materials & Design
High Entropy Alloys Studies
3.57
article

Accelerated design of eutectic high-entropy alloys using high-throughput phase diagram calculations and machine learning

J.X. Wang, Binggang Guan, Mingxu Wu, Da Shu, Chuan Zhang, Shubin Wang, Chao Yang, Baode Sun, Chengbo Xiao
article en
5 citations

Abstract

• A high-throughput phase diagram calculations plus machine learning approach was proposed to accelerate the design of eutectic-like dual-phase HEAs in a VNbTiSi-based system. • The design of experiments method was used to verify the eutectic-like duplex microstructure and to narrow down the data screening conditions. • A machine learning classification model that consists of eutectic, near-eutectic and non-eutectic compositions was established according to the eutectic-forming ability. • The proposed approach was evidenced in both five- and six-elemental multi-component alloys and expected to be extended to other alloy systems. Designing eutectic high-entropy alloys (HEAs) with a stable dual-phase structure is prospective for high-temperature structural materials, but remains a challenge due to the complexity of multicomponent compositional space. This study introduces a machine learning (ML) based classification model to assist the high-throughput thermodynamic calculations (HTCs), improving the efficiency by identifying eutectic, near-eutectic and non-eutectic compositions in the VNbTiTaSi system using the parameters that determine the eutectic forming ability. These parameters include the volume fraction of silicides, the liquidus temperature, the eutectic reaction temperature, the melting point, and the temperature interval, obtained from HTCs. The design of experiments (DOE) method was applied to verify the eutectic-like duplex microstructure and to narrow down the range of the abovementioned parameters. Ultimately, three eutectic alloys, V 36 Nb 31 Ti 13 Ta 5 Si 15 , V 32 Nb 29 Ti 17 Mo 7 Si 15 , and V 32 Nb 28 Ti 13 Ta 5 Mo 7 Si 15 , were selected from the eutectic zone identified by the ML models and demonstrated exceptional compression properties at elevated temperatures. This ML combining HTCs eutectic design method is expected to be extendable to multi-component alloys with even more elements and other alloy systems.

Materials & DesignVol. 254
Commercial Aircraft Corporation of China (China) (CN), Shanghai Jiao Tong University (CN), Beijing Institute of Aeronautical Materials (CN), Baotou Teachers College (CN), Baogang Group (China) (CN), CompuTherm (United States) (US), Baotou Research Institute of Rare Earths (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 7%
High Entropy Alloys Studies
3.57
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