Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts’ heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700% compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1869 materials out of 2000 are successfully optimized and deposited into the Carolina Materials Database
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 NbBrCl3are found with energy‐above‐hull values below 0.05 eV. CuBr3and GaBrO exhibit dynamic stability, confirmed by phonon dispersion analysis. In summary, the MTG pipeline shows significant potential for discovering new 2D and functional materials.
more » « less- NSF-PAR ID:
- 10482790
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 5
- Issue:
- 12
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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