The phase transition of cadmium selenide (CdSe) from wurtzite to rocksalt structure has been the subject of extensive research. In this study, we present a novel approach combining machine learning potentials with swarm intelligence-based pathway sampling to elucidate the complex phase transition mechanisms in CdSe. We developed an accurate machine-learning (ML) potential for CdSe, validated against density functional theory calculations, achieving mean absolute errors (MAEs) of 1.8 meV/atom for energies and 33 meV/Å for forces. This potential was integrated with the pathway sampling via swarm intelligence and graph theory (PALLAS) method to explore the potential energy landscape and identify low-energy transition pathways. Our simulations revealed a complex network of transition pathways, and we discovered a multi-step transition mechanism involving an unexpected zinc blende intermediate phase, which appears to play a crucial role in facilitating the transition between wurtzite and rocksalt structures. This finding provides new insights into the structural flexibility of CdSe and offers an explanation for experimentally observed phenomena such as wurtzite/zinc blende coexistence in nanostructures. Our approach not only advances the fundamental understanding of phase transitions in CdSe but also establishes a powerful computational framework for exploring complex materials phenomena, opening new avenues for materials design and discovery in semiconductor systems. 
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                            Influence of excess silicon on polytype selection during metal-mediated epitaxy of GaN nanowires
                        
                    
    
            We have examined the origins of polytype selection during metal-mediated molecular-beam epitaxy of GaN nanowires (NWs). High-angle annular dark-field scanning transmission electron microscopy reveals [111]-oriented zinc blende (ZB) NWs and [0001]-oriented wurtzite (WZ) NWs, with SixNy at the interface between individual NWs and the Si (001) substrate. Quantitative energy dispersive x-ray spectroscopy reveals a notably higher Si concentration of 7.0% ± 2.3% in zinc blende (ZB) NWs than 2.3% ± 1.2% in wurtzite (WZ) NWs. Meanwhile, density functional theory calculations show that incorporation of 8 at. % Si on the Ga sublattice inverts the difference in formation energies between WZ and ZB GaN, such that the ZB polytype of GaN is stabilized. This identification of Si and other ZB polytype stabilizers will enable the development of polytype heterostructures in a wide variety of WZ-preferring compounds. 
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                            - Award ID(s):
- 2240388
- PAR ID:
- 10624023
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- Applied Physics Letters
- Volume:
- 125
- Issue:
- 4
- ISSN:
- 0003-6951
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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