![]() To overcome the incompleteness and contingency of traditional “trial and error” methods and improve the alloy-development efficiency, the high-throughput preparation technique has been put forward, the core of which is to prepare a composition gradient to achieve a one-time characterization of a batch of compositions. Therefore, a significant challenge is how to select the most suitable composition more effectively, in order to obtain the most desired phase structure and property.Īn experiment was and remains one of the most effective methods in the search for novel alloys. However, their phase structures and properties are not as clearly related to the mixing entropy as originally designed. et al., 2018), the number of HEA compositions is increasing rapidly. In pursuit of more promising mechanical behaviors, such as ultrahigh strength and good cryogenic toughness that have been reported in some of the HEAs ( Gludovatz et al., 2014 Liang et al., 2018 Yang T. Stemming from the entropy-stabilizing effect to solid-solution phases, they are originally defined as single-phase multicomponent alloys, which consist of five or more elements in equal or near-equal atomic ratios ( Cantor et al., 2004 Yeh et al., 2004), but now the field expands to include intermetallics, nanoprecipitation, ceramic compounds, and non-equiatomic materials with as few as three principal elements ( Senkov et al., 2010 Zhang et al., 2014 Liu et al., 2016 Yang M. High-entropy alloys (HEAs) have attracted significant interest in recent years owing to their novel alloy-design principles. We illustrate the advantages, disadvantages, and application range of these techniques, and compare them with each other to provide some guidance for HEA study. The first-principles calculations are based on quantum mechanics and several open source databases, and it can also provide the finer atomic information for the thermodynamic analysis of CALPHAD and machine learning. The empirical model and the machine learning are both based on summary and analysis, while the latter is more believable for the use of multiple algorithms. Here we present and discuss four different calculation methods that are usually applied to accelerate the development of novel HEA compositions, that is, empirical models, first-principles calculations, calculation of phase diagrams (CALPHAD), and machine learning. ![]() In order to explore the huge compositional and microstructural spaces more effectively, high-throughput calculation techniques are put forward, overcoming the time-consuming and laboriousness of traditional experiments. ![]() High-entropy alloys (HEAs) open up new doors for their novel design principles and excellent properties. ![]() 5Qinghai Provincial Key Laboratory of New Light Alloys, Qinghai Provincial Engineering Research Center of High Performance Light Metal Alloys and Forming, Qinghai University, Xining, China.4Department of Materials Science and Engineering, The University of Tennessee, Knoxville, Knoxville, TN, United States.3State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an, China.2School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China.1Beijing Advanced Innovation Center of Materials Genome Engineering, State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing, China. ![]() Ruixuan Li 1 Lu Xie 2 William Yi Wang 3 Peter K. ![]()
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