Substrate-aware computational design of two-dimensional materials
Creators
- 1. Moscow Center for Advanced Studies, Kulakova str. 20, Moscow, 123592, Russian Federation
- 2. Emerging Technologies Research Center, XPANCEO, Internet City, Emmay Tower, Dubai, United Arab Emirates
- 3. Laboratory of Advanced Functional Materials, Yerevan State University, Yerevan, 0025 Armenia
- 4. Emanuel Institute of Biochemical Physics, Kosigina st. 4 Moscow, 119334, Russian Federation
- 5. Materials Discovery Laboratory, Skolkovo Institute of Science and Technology (Skoltech), Bolshoy Boulevard 30, bld. 1, Moscow, 121205, Russian Federation
- 6. National Graphene Institute (NGI), University of Manchester, Manchester, M13 9PL, UK
- 7. Department of Materials Science and Engineering, National University of Singapore, Singapore, 03-09 EA, Singapore
- 8. Institute for Functional Intelligent Materials, National University of Singapore, Singapore, 117544, Singapore
* Contact person
Description
Two-dimensional (2D) materials have attracted considerable attention due to their remarkable electronic, mechanical and optical properties, making them prime candidates for next-generation electronic and optoelectronic applications. Despite their widespread use in combination with substrates in practical applications, including the fabrication process and final device assembly, computational studies often neglect the effects of substrate interactions for simplicity. In this record, we provide the results of the computational study of the stable 2D molybdenum-sulfur (Mo-S) structures on a c-cut sapphire (Al₂O₃). In particular, we provide the results of the evolutionary search in the Mo-S / Al₂O₃ (0001) system, the machine learning interatomic potential (MLIP) used for local relaxation of the systems during the evolutionary search together with its training set, post-processing data on electronic and phonon band structures of the stable 2D Mo-S structures, and the predicted stability patterns from the perspective of CVD synthesis.
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References
Preprint (Main paper in which the method is described and within which the data is generated) A. Mazitov, I. Kruglov, A. V. Yanilkin, A. V. Arsenin, V. S. Volkov, D. G. Kvashnin, A. R. Oganov, K. S. Novoselov, arXiv:2408.08663 (2024), doi: 10.48550/arXiv.2408.08663