Symmetry-based computational search for novel binary and ternary 2D materials

We present a symmetry-based systematic approach to explore the structural and compositional richness of two-dimensional materials. We use a 'combinatorial engine' that constructs candidate 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 A( n )B( m ) or A( n )B( m )C( k ). Among the discovered structures, we find examples that can be built by decorating nearly all Platonic and Archimedean tessellations 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 systematic search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with a distance to the convex hull of thermodynamic stability smaller than 250 meV/atom.

Automated summary

We propose a symmetry-based systematic approach to explore the variety of two-dimensional materials. By using a 'combinatorial engine', we are able to construct candidate compounds based on their structural positions and combinations of chemical elements. We discover a wide range of two-dimensional materials, covering different stoichiometries and exhibiting polymorphism. Machine learning techniques are then employed to accelerate the exploration of the chemical space of two-dimensional materials. Our approach reveals around 6500 new compounds that were not present in previous databases.