Quantifying Chemical Short-Range Order in Metallic Alloys

High-entropy materials are metallic or ceramic systems composed of three or more chemical elements mixed in nearly equiatomic concentrations. These design choices lead to substantial chemical complexity which functions as the background against which microstructural evolution occurs, thereby affecting various material properties through chemistry–microstructure relationships. However, computationally capturing and defining this complexity has remained a challenge and is often overlooked. Here, I will discuss how machine learning, group theory, and statistical mechanics, can be employed together to characterize, atom-by-atom, the state of short-range order of high-entropy materials, thereby advancing the quantitative understanding of metallic alloys, and paving the way for the rigorous incorporation of this phenomenon into mechanical and thermodynamic models.