Hai-Chen Wang¹, Jonathan Schmidt¹, Miguel A. L. Marques^1*, Ludger Wirtz^2*, Aldo H. Romero^3,2*

1 Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, 06120 Halle (Saale), Germany.

2 Department of Physics and Materials Science, University of Luxembourg, 162a avenue de la Faïencerie, L-1511 Luxembourg, Luxembourg

3 Department of Physics, West Virginia University, Morgantown, WV 26506, USA

* Corresponding authors emails: miguel.marques@physik.uni-halle.de, ludger.wirtz@uni.lu, Aldo.Romero@mail.wvu.edu

DOI10.24435/materialscloud:sb-cy [version v1]

Publication date: Nov 21, 2022

How to cite this record

Hai-Chen Wang, Jonathan Schmidt, Miguel A. L. Marques, Ludger Wirtz, Aldo H. Romero, Symmetry-based computational search for novel binary and ternary 2D materials, Materials Cloud Archive 2022.154 (2022), https://doi.org/10.24435/materialscloud:sb-cy

Description

We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a combinatorial engine' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form AnBm or AnBmCk. Among the found structures we find examples that can be built by decorating nearly all Platonic and Archimedean tesselations as well as their dual Laves or Catalan tilings. We also obtain a rich, and unexpected, polymorphism for some specific compounds. We further accelerate the exploration of the chemical space of two-dimensional materials by employing machine-learning-accelerated prototype search, based on the structural types discovered in the exhaustive search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with an energy of less than 250 meV/atom above the convex hull of thermodynamic stability.

Materials Cloud sections using this data

Files

File name	Size	Description
2_spg01.json.bz2 MD5md5:00e5939026ba64e29ee926ec08c92691	13.5 KiB	Binary compounds stemming from the systematic search with space group number 1
2_spg04.json.bz2 MD5md5:19770b2291f8ae3cf15d0f700b86da37	291.5 KiB	Binary compounds stemming from the systematic search with space group number 4
2_spg05.json.bz2 MD5md5:50770e64a550a2ac4d9c714983e7448e	42.7 KiB	Binary compounds stemming from the systematic search with space group number 5
2_spg08.json.bz2 MD5md5:965f07933e7848d0c1afe562db5680db	868.8 KiB	Binary compounds stemming from the systematic search with space group number 8
2_spg09.json.bz2 MD5md5:29325e46b5996ddc340e09b8c4407bd8	33.7 KiB	Binary compounds stemming from the systematic search with space group number 9
2_spg10.json.bz2 MD5md5:84f91c00f8baa4ffd24070df06c28fcb	193.9 KiB	Binary compounds stemming from the systematic search with space group number 10
2_spg12.json.bz2 MD5md5:96e545deaae8343a02d34be7135e012f	41.5 KiB	Binary compounds stemming from the systematic search with space group number 12
2_spg13.json.bz2 MD5md5:6b92b98845b4d4bc7ec6597e11c40453	486.2 KiB	Binary compounds stemming from the systematic search with space group number 13
2_spg17.json.bz2 MD5md5:84c782874e5b8b7c0e343b52f1a3cde4	365.7 KiB	Binary compounds stemming from the systematic search with space group number 17
2_spg21.json.bz2 MD5md5:c58f9a815505df463e1c0fbdc0329391	456.6 KiB	Binary compounds stemming from the systematic search with space group number 21
2_spg29.json.bz2 MD5md5:a2334f1ca05420d43da12919a52893fa	170.5 KiB	Binary compounds stemming from the systematic search with space group number 29
2_spg30.json.bz2 MD5md5:bd39d79700645816588a5d13b298961c	362.8 KiB	Binary compounds stemming from the systematic search with space group number 30
2_spg31.json.bz2 MD5md5:50c3ab0c1ae01100f152cdce06310047	685.8 KiB	Binary compounds stemming from the systematic search with space group number 31
2_spg32.json.bz2 MD5md5:5387dbcfef307d3a9ee8c7f8caeb9a69	169.2 KiB	Binary compounds stemming from the systematic search with space group number 32
2_spg33.json.bz2 MD5md5:237ae02123724cfa65c25424be76fa8f	21.6 KiB	Binary compounds stemming from the systematic search with space group number 33
2_spg34.json.bz2 MD5md5:10aee9fd9271ba2a4b64e96a35e08d91	117.3 KiB	Binary compounds stemming from the systematic search with space group number 34
2_spg35.json.bz2 MD5md5:66a7c303a5966e5f6a47b97893b50c95	53.0 KiB	Binary compounds stemming from the systematic search with space group number 35
2_spg36.json.bz2 MD5md5:9135b4d780e4de1dadb8ce3e9d7395d4	852.0 KiB	Binary compounds stemming from the systematic search with space group number 36
2_spg70.json.bz2 MD5md5:40a20266e42f9bb384df38ff45c7ac50	3.9 MiB	Binary compounds stemming from the systematic search with space group number 70
3_spg05.json.bz2 MD5md5:dc5afc4b65b756384130233ebf0ab2db	366.6 KiB	Ternary compounds stemming from the systematic search with space group number 5
3_spg09.json.bz2 MD5md5:9e905905c32ab5f595c1e77cc171bfbc	329.0 KiB	Ternary compounds stemming from the systematic search with space group number 9
3_spg12.json.bz2 MD5md5:a9828bb1f6c1fef9ff247478799e254d	748.6 KiB	Ternary compounds stemming from the systematic search with space group number 12
3_spg33.json.bz2 MD5md5:5cf33e561f1ab7748e07462d44689bc9	253.1 KiB	Ternary compounds stemming from the systematic search with space group number 33
ml_2_A2B5.json.bz2 MD5md5:72ae010b3b21b7ca6efe1a8b845eaf83	122.8 KiB	Binary compounds stemming from crystal-graph networks with composition A2B5
ml_2_A2B7.json.bz2 MD5md5:34ac7ae03cfb6e4494ae0cba6ca86c5f	87.5 KiB	Binary compounds stemming from crystal-graph networks with composition A2B7
ml_3_A2B2C3.json.bz2 MD5md5:764737232ffbf5adcbf277ee80a40ab9	2.9 KiB	Ternary compounds stemming from crystal-graph networks with composition A2B2C3
ml_3_AB2C2.json.bz2 MD5md5:10dd31edced60badb917645c003487b3	125.9 KiB	Ternary compounds stemming from crystal-graph networks with composition AB2C2
ml_3_ABC2.json.bz2 MD5md5:8922fb10976cebf999db9450769fa4d7	21.1 KiB	Ternary compounds stemming from crystal-graph networks with composition ABC2
ml_3_ABC3.json.bz2 MD5md5:43d8a3dfd32098523c2d286c1ecb4ec3	145.2 KiB	Ternary compounds stemming from crystal-graph networks with composition ABC3

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

high-throughput density-functional theory 2D materials machine learning crystal-graph neural networks