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Journal of Materials Informatics

C O NT E NT S

Topic: Machine Learning Approach for Design, Development and
Application of High Entropy Materials

1 New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from
single-phase to multiphase systems
Yinghao Zhou, Zehuan Zhang, Dawei Wang, Weicheng Xiao, Jiang Ju, Shaofei Liu, Bo Xiao, Ming Yan, Tao Yang*
J Mater Inf 2023;2:18 http://dx.doi.org/10.20517/jmi.2022.27

2 High-entropy alloy catalysts: high-throughput and machine learning-driven design
Lixin Chen, Zhiwen Chen, Xue Yao, Baoxian Su, Weijian Chen, Xin Pang, Keun-Su Kim, Chandra Veer Singh*,
Yu Zou*
J Mater Inf 2022;2:19 http://dx.doi.org/10.20517/jmi.2022.23

3 Identifying stress-induced heterogeneity in Cu Zr Ni Ti Pd high-entropy metallic glass from machine
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learning atomic dynamics
Xiaodi Liu, Wenfei Lu, Wenkang Tu, Jun Shen*
J Mater Inf 2022;2:20 http://dx.doi.org/10.20517/jmi.2022.29
4 Data-driven prediction of the glass-forming ability of modeled alloys by supervised machine learning
Yuan-Chao Hu*, Jiachuan Tian
J Mater Inf 2023;3:1 http://dx.doi.org/10.20517/jmi.2022.28

5 A review on high-throughput development of high-entropy alloys by combinatorial methods
Shahryar Mooraj, Wen Chen*
J Mater Inf 2023;3:4 http://dx.doi.org/10.20517/jmi.2022.41

6 Data-driven design of eutectic high entropy alloys
Zhaoqi Chen, Yong Yang*
J Mater Inf 2023;3:10 http://dx.doi.org/10.20517/jmi.2023.06