Explicit Demonstration of Short-Range Order Through Synergistic Modeling-Characterization Investigation

The significance of short-range order (SRO) in high-entropy materials (HEM) has been recently recognized but the intricacy and subtlety of SRO make it highly non-trivial to characterize SRO through conventional characterization approaches. Even the state-of-the-art characterization techniques are often shown insufficient to explicitly demonstrate the existence of SRO in HEM. For example, 4D scanning transmission electron microscopy (4D-STEM) can detect SRO at nanoscale through the appearance of diffuse diffraction patterns but multiple sources have been shown to potentially contribute to such diffraction signals. Atom-probe tomography (APT) probes both chemical and positional information of each atom in alloy’s lattice, but its limited spatial resolution restricts APT’s applicability to retrieve meaningful information of SRO in alloys. Therefore, an explicit demonstration of SRO requires synergistic efforts from both characterization and modeling on the same spatial scale. To achieve this goal, we recently developed two new modeling capabilities: (1) A physics-informed, statistical approach to recover Warren-Cowley SRO parameter in alloy from noisy APT data, and (2) A highly efficient and accurate machine-learning potential framework¹ to model SRO in alloys at the same scale of 4D-STEM and APT. Applying these new capabilities through a collaboration with advanced characterizations, we explicitly demonstrate the existence of SRO in group IV alloys, verifying our early theoretical predictions^2,3. Importantly, this modeling-characterization synergy further enables mapping the spatially resolved diffuse diffraction patterns in 4D-STEM to different local SRO structural motifs, presenting a first-of-its-kind demonstration of in-depth investigation of SRO in any alloys. The developed approaches are also shown to be applicable in other alloys, thus opening the possibility of explicitly characterizing SRO in a wide range of HEM.<br/><br/>This work is supported by Department of Energy, Office of Basic Energy of Sciences under Award No. DE-SC0023412.<br/><br/><br/><br/>(1) Chen, S.; Jin, X.; Zhao, W.; Li, T. Intricate Short-Range Order in GeSn Alloys Revealed by Atomistic Simulations with Highly Accurate and Efficient Machine-Learning Potentials. <i>Phys. Rev. Mater.</i> <b>2024</b>, <i>8</i> (4), 043805. https://doi.org/10.1103/physrevmaterials.8.043805.<br/>(2) Cao, B.; Chen, S.; Jin, X.; Liu, J.; Li, T. Short-Range Order in GeSn Alloy. <i>ACS Appl. Mater. Interfaces</i> <b>2020</b>, <i>12</i>, 57245–57253. https://doi.org/10.1021/acsami.0c18483.<br/>(3) Jin, X.; Chen, S.; Li, T. Coexistence of Two Types of Short-Range Order in Si–Ge–Sn Medium-Entropy Alloys. <i>Commun Mater</i> <b>2022</b>, <i>3</i> (1), 66. https://doi.org/10.1038/s43246-022-00289-5.