A review on high-throughput development of high-entropy alloys by combinatorial methods

Abstract

High-entropy alloys (HEAs) are an emerging class of alloys with multi-principal elements that greatly expands the compositional space for advanced alloy design. Besides chemistry, processing history can also affect the phase and microstructure formation in HEAs. The number of possible alloy compositions and processing paths gives rise to enormous material design space, which makes it challenging to explore by traditional trial-and-error approaches. This review highlights the progress in combinatorial high-throughput studies towards rapid prediction, manufacturing, and characterization of promising HEA compositions. This review begins with an introduction to HEAs and their unique properties. Then, this review describes high-throughput computational methods such as machine learning that can predict desired alloy compositions from hundreds or even thousands of candidates. The next section of this review presents advances in combinatorial synthesis of material libraries by additive manufacturing for efficient development of high-performance HEAs at bulk scale. The final section of this review discusses the high-throughput characterization techniques that are used to accelerate the material property measurements for systematic understanding of the composition-processing-structure-property relationships in combinatorial HEA libraries.

High‐entropy (Ti0.2V0.2Nb0.2Mo0.2W0.2)Si2 with excellent high‐temperature wear resistance

“…However, VCA deals with this problem by using a pseudo-potential that averages the properties of each atom at different positions in the lattice cell. 26 The results obtained by VCA are closer to the experimental values as can be seen in the study by Ramer and Rappe. 27 Meanwhile, Chen et al verified the accuracy of the VCA method in predicting the lattice elastic constants and elastic properties of the Ti x VNbMo system by comparing it with experiments.…”

“…SQS and SLAE are required to construct a supercell, which is very burdensome in DFT calculations. However, VCA deals with this problem by using a pseudo‐potential that averages the properties of each atom at different positions in the lattice cell 26 . The results obtained by VCA are closer to the experimental values as can be seen in the study by Ramer and Rappe 27 .…”

Future prospects of high-entropy alloys as next-generation industrial electrode materials

“…Interestingly, the calculation of phase diagrams (CALPHAD) method is an alternative technique for predicting equilibrium phases and a direct strategy for designing HEAs. 217,218 However, the applicability of the CALPHAD method is limited by the accuracy of the database, and given the chemical complexity of HEMs, the DFT-based methods are inherently better suited for energy calculations. DFT analysis proves valuable in the design of quaternary high-entropy OOH catalysts, revealing those with the lowest adsorption free energy for the oxygen evolution reaction (OER) process (Fig.…”

“…215 Several computational methods are available, including DFT, molecular dynamics (MD), discrete dislocation dynamics, the phase field method, the finite element method, and the thermodynamic method. 45,216,217 HEA phase stability and phase diagrams can be obtained using DFT or MD, or they can be obtained from the Gibbs free energy calculations based on the first-principles calculations using phase diagram calculation methods. Interestingly, the calculation of phase diagrams (CALPHAD) method is an alternative technique for predicting equilibrium phases and a direct strategy for designing HEAs.…”

Combinatorial Design of Novel Multiprincipal Element Alloys Using Experimental Techniques

“…Instead, mapping of the structure and properties in a compositional library is easier when a combinatorial specimen is utilized. [ 14 ] Using fast characterization techniques such as synchrotron X‐ray diffraction (XRD) and instrumented indentation on combinatorial samples, the correlation between the composition and the features of MPEAs can be successfully revealed. [ 15 ] In this study, former experimental results obtained on the processing and characterization of combinatorial MPEAs are critically overviewed and the envisioned future research directions in this field are described.…”