Discovery of 2D Materials using Transformer Network‐Based Generative Design

Abstract

Two‐dimensional (2D) materials offer great potential in various fields like superconductivity, quantum systems, and topological materials. However, designing them systematically remains challenging due to the limited pool of fewer than 100 experimentally synthesized 2D materials. Recent advancements in deep learning, data mining, and density functional theory (DFT) calculations have paved the way for exploring new 2D material candidates. Herein, a generative material design pipeline known as the material transformer generator (MTG) is proposed. MTG leverages two distinct 2D material composition generators, both trained using self‐learning neural language models rooted in transformers, with and without transfer learning. These models generate numerous potential 2D compositions, which are plugged into established templates for known 2D materials to predict their crystal structures. To ensure stability, DFT computations assess their thermodynamic stability based on energy‐above‐hull and formation energy metrics. MTG has found four new DFT‐validated stable 2D materials: NiCl4, IrSBr, CuBr3, and CoBrCl, all with zero energy‐above‐hull values that indicate thermodynamic stability. Additionally, GaBrO and NbBrCl3 are found with energy‐above‐hull values below 0.05 eV. CuBr3 and GaBrO exhibit dynamic stability, confirmed by phonon dispersion analysis. In summary, the MTG pipeline shows significant potential for discovering new 2D and functional materials.

Keywords

• crystal structure prediction
• deep learning
• transformer neural networks
• materials discovery
• 2D materials
• transformer-based materials generators