Arslan Mazitov^1*, Ivan Kruglov^1,2, Alexey V. Yanilkin¹, Aleksey V. Arsenin^1,2,3, Valentyn S. Volkov^2,3, Dmitry G. Kvashnin⁴, Artem R. Oganov⁵, Kostya S. Novoselov^6,7,8

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

* Corresponding authors emails: arslan.mazitov@phystech.edu

DOI10.24435/materialscloud:8q-a1 [version v1]

Publication date: Aug 19, 2024

How to cite this record

Arslan Mazitov, Ivan Kruglov, Alexey V. Yanilkin, Aleksey V. Arsenin, Valentyn S. Volkov, Dmitry G. Kvashnin, Artem R. Oganov, Kostya S. Novoselov, Substrate-aware computational design of two-dimensional materials, Materials Cloud Archive 2024.124 (2024), https://doi.org/10.24435/materialscloud:8q-a1

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.

Materials Cloud sections using this data

Files

File name	Size	Description
vasp.zip MD5md5:f6bab99c8ad3cb45e34103273f7d08e8	832 Bytes	A zipped folder with the VASP INCAR files used for training the MLIP and re-evaluating the stable structures from the evolutionary search
uspex.zip MD5md5:304087ce3b132104a6cad1ef08d393ad	1.9 KiB	A zipped folder with the USPEX input files used for evolutionary search for stable structures in the 2D Mo-S/Al2O3 system
mlip.zip MD5md5:2e5f42c28cccc601334b687fa55bc34a	50.4 MiB	A zipped folder with the trained MTP potential and the training dataset with 3 different classes of systems used for training the MLIP within the ASCT approach.
lammps.zip MD5md5:40af97ea49bd54cdcbf050ba7322fff0	1.4 KiB	A zipped folder with the input files for the LAMMPS code, that were used for local relaxation of 2D Mo-S structures during the evolutionary search
notebooks.zip MD5md5:960c2f36cef8de8ef9f29360e2f3d004	26.2 MiB	A zipped folder with Jupyter Notebooks and raw calculations data used for making the raw figures in the original paper
structures.zip MD5md5:d9d0bc6dc1494030709fc6239b7f1829	6.8 MiB	A zipped folder containing the results of the evolutionary search stored as a list of files in the extended XYZ format.
README.md MD5md5:88f0ae7753e78baa7a7c3c9e827b27de	5.5 KiB	A README file containing a description of the data record

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

2D materials machine learning crystal structure prediction