Among the diversity of new materials, two-dimensional crystal structures have been attracting significant attention from the broad scientific community due to their promising applications in nanoscience. In this study we predict a novel two-dimensional ferromagnetic boron material, which has been exhaustively studied with DFT methods. The relaxed structure of the 2D-B 6 monolayer consists of slightly flattened octahedral units connected with 2c-2e B–B σ-bonds. The calculated phonon spectrum and ab initio molecular dynamics simulations reveal the thermal and dynamical stability of the designed material. The calculation of the mechanical properties indicate a relatively high Young's modulus of 149 N m −1 . Moreover, the electronic structure indicates the metallic nature of the 2D-B 6 sheets, whereas the magnetic moment per unit cell is found to be 1.59 μ B . The magnetism in the 2D-B 6 monolayer can be described by the presence of two unpaired delocalized bonding elements inside every distorted octahedron. Interestingly, the nature of the magnetism does not lie in the presence of half-occupied atomic orbitals, as was shown for previously studied magnetic materials based on boron. We hope that our predictions will provide promising new ideas for the further fabrication of boron-based two-dimensional magnetic materials.
Computational Discovery of New 2D Materials Using Deep Learning Generative Models
Two-dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. Although several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains challenging. Herein, we propose a deep learning generative model for composition generation combined with a random forest-based 2D materials classifier to discover new hypothetical 2D materials. Furthermore, a template-based element substitution structure prediction approach is developed to predict the crystal structures of a subset of the newly predicted hypothetical formulas, which allows us to confirm their structure stability using DFT calculations. So far, we have discovered 267 489 new potential 2D materials compositions, where 1485 probability scores are more then 0.95. Among them, we have predicted 101 crystal structures and confirmed 92 2D/layered materials by DFT formation energy calculation. Our results show that generative machine learning models provide an effective way to explore the vast chemical design space for new 2D materials discovery.
- Publication Date:
- NSF-PAR ID:
- 10288156
- Journal Name:
- ACS Applied Materials & Interfaces
- ISSN:
- 1944-8244
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1021/acsami.1c01044
