Advanced quantifying and tuning short-range order parameters in alloys: a review

Abstract Understanding the impact of short-range order (SRO) on the quality of alloys is of paramount importance for high-end industrial applications. Providing an accurate quantification of the SRO with respect to small variations of degrees of freedom such as composition modulation and preparation conditions, as well as different post-processing treatments, could facilitate considerably the design of alloys and fine tailor their specific properties, implying a serious economy of both time and resources. In this review, the significant implications of SRO on fundamental and physical properties, as well as the most recent advances in terms of experimental and computational analysis of SRO, are treated. The importance of considering the spatial distribution of elements, or the structure motifs, offering a more global image relative to typical Warren-Cowley SRO parameters, in order to improve the reliability of the SRO-property relation and the accuracy of predictions, is highlighted. The capabilities offered by machine learning potentials to performing highly accurate computations for extended systems (up to billions of atoms), at suitable operational timeframes are explored. This review further focuses on the different degrees of freedom (annealing, thermomagnetic treatments, deformation) allowing tailoring the SRO parameters and, consequently, resulting in the fine tuning of the physical properties of alloys. Major controversies, regarding the influence of SRO on the physical properties, were recently elucidated through experimental or computational validation and are discussed here. Overview and perspectives to strategically overcome the present challenges and further improve the alloy design flexibility and tunability for specific advanced applications, are considered.