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

2022/12/07 by Hai‐Chen Wang, Wang, Hai-Chen, Jonathan Schmidt +7 · 8 citations
Materials Science · #2D Materials and Applications #Boron and Carbon Nanomaterials Research #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)

paper · pdf · doi:10.48550/arxiv.2212.03975

openalex publication_date 2022/12/07 · openalex created_date 2022/12/22 · openalex updated_date 2026/07/28

Abstract

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 A_nB_m or A_nB_mC_k. 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.

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