Physics

Organic chemistry

Wireless

Entropy (arrow of time)

Machine learning

Throughput

Chemistry

High entropy alloys

Computer science

Phase (matter)

Telecommunications

CALPHAD

Phase diagram

Thermodynamics

Frontiers in Materials

High-entropy alloys (HEAs) open up new doors for their novel design principles and excellent properties. 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. 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. 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. 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. We illustrate the advantages, disadvantages, and application range of these techniques, and compare them with each other to provide some guidance for identifying the appropriate methods for specific HEA studies.

High-Throughput Calculations for High-Entropy Alloys: A Brief Review