Discovery of 2D materials using Transformer Network based Generative Design
This work addresses the challenge of discovering new 2D materials for applications in superconductors and quantum materials, but it is incremental as it builds on existing generative and computational methods.
The authors tackled the problem of rational design for 2D materials by proposing a generative pipeline called MTG, which uses Transformer-based models to generate candidate compositions and DFT computations to verify stability, resulting in the discovery of four new DFT-verified stable 2D materials with zero energy-above-hull.
Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently less than 6,000 experimentally synthesized 2D materials have been reported. Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications. Here we propose a generative material design pipeline, namely material transformer generator(MTG), for large-scale discovery of hypothetical 2D materials. We train two 2D materials composition generators using self-learning neural language models based on Transformers with and without transfer learning. The models are then used to generate a large number of candidate 2D compositions, which are fed to known 2D materials templates for crystal structure prediction. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. We report four new DFT-verified stable 2D materials with zero e-above-hull energies, including NiCl$_4$, IrSBr, CuBr$_3$, and CoBrCl. Our work thus demonstrates the potential of our MTG generative materials design pipeline in the discovery of novel 2D materials and other functional materials.