Abstract: The formation of L1₂-chemical ordering is a critical phase transformation phenomenon in face-centered cubic (FCC) compositionally complex alloys (CCAs) that can significantly influence their properties. However, quantifying the early stages of chemical ordering is challenging due to the complex elemental arrangements, small domain sizes, similar atomic numbers, and subtle compositional differences from the surrounding matrix. We recently introduced a machine learning-enhanced atom probe tomography (ML-APT) approach to characterize chemical short-range ordering (CSRO) in binary and ternary alloy systems. Here, this methodology is extended to the more complex CoCrNiFeMn alloys, investigating the impact of different compositions and heat treatments on the formation of L1_2-type CSRO. Our findings offer solid tomographic evidence of L1_2-CSRO, primarily with Cr repulsion tendency, along with domain size and morphology distributions that align with reported Monte Carlo simulations. The extent of L1_2-CSRO is strongly dependent on both composition and heat treatment conditions. A standard workflow for applying ML-APT to CCAs is proposed, emphasizing its strengths and limitations, and setting the stage for further exploration and control of local chemical ordering to advance the design of CCAs.

Key words: Local chemical ordering, High-entropy alloy, Atomic-scale characterization, Machine learning, Alloy design