Bridging the Gap between Modeling and Characterization to Unravel Short-Range Order and Properties of Si-Ge-Sn-Pb Alloys

Group IV concentrated alloys composed of Si-Ge-Sn-Pb are promising candidates for mid-infrared photonics owing to their tunable band gaps, low-cost, and CMOS-compatibility. Although group IV alloys have been long conceived as a random solid solution, our recent ab initio-based statistical sampling predicted substantial short-range order (SRO) behaviors in group IV alloys ^1–4. The structural complexity was further shown theoretically to yield a substantial impact on the underlying electronic^1–3, topological⁴, and transport properties of group IV alloys. Although SRO may have a complicated implication on material quality, it also opens an emerging opportunity for engineering SRO to harvest new functionalities that are challenging to achieve through traditional heterojunctions. Emerging characterization evidence based on EXAFS⁵, Raman⁶, APT⁷ and 4D-STEM support our prediction of SRO, but important questions need to be addressed regarding the actual structures, spatial domain size and its distribution, and corresponding changes in the properties of SRO, before a practical strategy can be employed. The explicit answers to these questions require synergistic efforts from both characterization and modeling. Here I will discuss our ongoing theoretical effort to close the gap between theory and characterization to enable deciphering the fine structural details of complex alloys. We have developed a highly accurate, highly efficient machine-learning potential (MLP) based on neuroevolution potential framework for group IV system. The developed MLP is shown to reach a DFT-level accuracy by exhibiting a root mean squared error of energy &lt; 1 meV/atom with respect to the DFT training data set and more importantly, enables a side-by-side comparison with APT and 4D-STEM on the same scale. Using this development, we discovered the spatial structural heterogeneity in group IV alloys. In particular, we show that structural details at a fine level, as reflected by the distributions of both atomic SRO parameter and spatial SRO domains, are vital for the underlying electronic structures of group IV concentrated alloys.<br/><br/>This work is supported by Department of Energy, Office of Basic Energy of Sciences under Award No. DE-SC0023412.<br/><br/><br/>(1) 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.<br/>(2) 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.<br/>(3) Jin, X.; Chen, S.; Li, T. Short-Range Order in SiSn Alloy Enriched by Second-Nearest-Neighbor Repulsion. <i>Phys Rev Mater</i> <b>2021</b>, <i>5</i> (10), 104606.<br/>(4) Liang, Y.; Chen, S.; Jin, X.; West, D.; Yu, S.-Q.; Li, T.; Zhang, S. Group IV Topological Quantum Alloy and the Role of Short-Range Order: The Case of Ge-Rich GePb, Under Review 2023.<br/>(5) Lentz, J. Z.; Woicik, J. C.; Bergschneider, M.; Davis, R.; Mehta, A.; Cho, K.; McIntyre, P. C. Local Ordering in Ge/Ge–Sn Semiconductor Alloy Core/Shell Nanowires Revealed by Extended x-Ray Absorption Fine Structure (EXAFS). <i>Appl Phys Lett</i> <b>2023</b>, <i>122</i> (6), 062103.<br/>(6) Corley-Wiciak, A. A.; Chen, S.; Concepción, O.; Zoellner, M. H.; Grützmacher, D.; Buca, D.; Li, T.; Capellini, G.; Spirito, D. Local Alloy Order in a Ge1−xSnx/Ge Epitaxial Layer. <i>Phys. Rev. Appl.</i> <b>2023</b>, <i>20</i> (2), 024021.<br/>(7) Liu, S.; Covian, A. C.; Wang, X.; Cline, C. T.; Akey, A.; Dong, W.; Yu, S.; Liu, J. 3D Nanoscale Mapping of Short-Range Order in GeSn Alloys. <i>Small Methods</i> <b>2022</b>, 2200029.