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Topic: Machine Learning Approach for Design,

Development and Application of High Entropy

Materials

Prof. Yong Yang, Prof. Sheng Guo, Prof. Wen Chen,
Department of Mechanical Department of Mechanical
Department of Mechanical EngineerinDepartment of Industrial EngineerinDepartment of Mechanical
Engineering, City University of Hong and Materials Science, Chalmers and Industrial Engineering,
Kong, Kowloon Tong, Kowloon, University of Technology, University of Massachusetts,
Hong Kong, China. Gothenburg, Sweden. Amherst, MA, USA.
E-mail: yonyang@cityu.edu.hk E-mail: sheng.guo@chalmers.se E-mail: wenchen@umass.edu

Special Issue Introduction:

Since their advent in 2004, high entropy alloys that comprise more than five
principal elements have been attracting tremendous research interest worldwide.
Unlike traditional alloys based on one or rarely two principal elements, high
entropy alloys are well known for their compositional complexity but yet, a

large number of them still show an overall simple solid solution structure. This
phenomenon is often attributed to a “high mixing entropy” effect, which, in
principle, favors random mixing of materials’ building blocks (e.g., atoms, ions,

molecules) over chemical ordering or de-mixing at high temperatures. Therefore,
it can be conceived that high entropy alloys are likely to be thermodynamically
metastable at ambient temperature, and this structural metastability may impart