OBRABOTKAMETALLOV MATERIAL SCIENCE Vol. 26 No. 4 2024 temperatures. This discovery may be an important contribution to theoretical research and applications in high-temperature anti-softening. High yield strength and ultimate strength were noted in a study on Mo-based HEAs. The compressive yield strength of the M20 alloy reaches up to 1,285 MPa, the ultimate strength is 2,447 MPa, and the elongation is 27 % [52]. Recent research conducted at Belgorod State University [53] resulted in the development of a new HEA, Co40Mo28Nb25Hf7, which demonstrated outstanding mechanical properties at high temperatures. This alloy, produced by vacuum arc remelting, includes BCC and Laves C14 phases, as well as a small amount of hafnium oxides. Studies have shown that the alloy has a high yield strength at room temperature (1,775 MPa) and retains signifi cant strength at 1,000 °C (600 MPa). In the temperature range of 22–1,000 °C, its specifi c strength surpasses many commercial superalloys and other HEAs, highlighting its potential for high-temperature applications. Methods for Improving Strength Properties Improving the strength properties of HEAs can be achieved by various methods, each of which is aimed at optimizing the microstructure and phase composition of the materials. One such method is the introduction of new gradient nanoscale structures of dislocation cells into a stable single-phase face-centered cubic (FCC) lattice. The face-centered cubic (FCC) lattice is a crystalline structure in which atoms are located at the corners and in the center of each face of the cube. This confi guration provides the material with high plasticity and the ability to deform. Dislocation cells, being areas of local deformation in the crystal lattice, create additional resistance to dislocation movement, which increases the strength of the material without an obvious loss of plasticity [54]. The process of introducing such structures includes thermomechanical treatment, controlled cooling, or the use of nanoscale additives that promote the formation of dislocation cells with specifi c characteristics. As a result, HEAs with FCC lattices and gradient structures demonstrate improved performance, making them promising for use under high loads. Another method is cold rolling followed by laser surface heat treatment. Cold rolling is a process of deforming a material at low temperatures, which strengthens the material due to the increase in the density of dislocations. Laser surface heat treatment involves the use of a laser for local heating and subsequent cooling of the material, which allows to modify its microstructure and improve mechanical properties [55]. Spinodal decomposition, which causes compositional heterogeneity in the structure, is the process of separating a solid solution into two phases with diff erent chemical compositions. As a result of spinodal decomposition, nanometer-scale structures are formed that strengthen the material. This compositional heterogeneity signifi cantly enhances the mechanical characteristics of HEAs, making it stronger and more reliable for use under high loads and temperatures [56]. The use of laser additive manufacturing for coherent strengthening of alloys is another eff ective method. Laser additive manufacturing is a technology where material is added layer by layer using a laser. This method allows precise control of the microstructure and phase composition of the material, leading to improved strength properties [57]. Thus, the implementation of these methods signifi cantly improves the strength properties of HEAs, ensuring high strength and maintaining plasticity, making it promising for use in various high-load and high-temperature applications. Property Prediction and Modeling Research into increasing the strength of HEAs is of strategic importance for creating advanced materials that combine strength, low density, and resistance to various operational conditions. Research can be found on predicting the strength of HEAs, in particular based on machine learning. Machine learning (ML) is a branch of artifi cial intelligence that trains computer systems to perform tasks without being explicitly programmed to do so. Instead of using explicit instructions, machines learn from data and algorithms, identifying patterns and making predictions or decisions. In the fi eld of materials science and nanotechnology, multiscale modeling has become an essential tool for understanding material properties across diff erent levels – from atomic to macroscopic. The use of
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