Discovery of 2D Materials via Symmetry-Constrained Diffusion Model

TL;DR

This paper introduces a symmetry-constrained diffusion model for generating 2D materials, improving diversity and stability by integrating symmetry principles, and successfully identifies stable candidate structures validated by DFT calculations.

Contribution

The paper presents a novel symmetry-constrained diffusion model that incorporates space group symmetry into the generative process for 2D materials, enhancing the quality and stability of generated structures.

Findings

Generated 2,000 candidate structures with the model.

843 structures met energy stability criteria (Ehull < 0.6 eV/atom).

Six candidates showed phonon stability and promising properties.

Abstract

Generative model for 2D materials has shown significant promise in accelerating the material discovery process. The stability and performance of these materials are strongly influenced by their underlying symmetry. However, existing generative models for 2D materials often neglect symmetry constraints, which limits both the diversity and quality of the generated structures. Here, we introduce a symmetry-constrained diffusion model (SCDM) that integrates space group symmetry into the generative process. By incorporating Wyckoff positions, the model ensures adherence to symmetry principles, leading to the generation of 2,000 candidate structures. DFT calculations were conducted to evaluate the convex hull energies of these structures after structural relaxation. From the generated samples, 843 materials that met the energy stability criteria (Ehull < 0.6 eV/atom) were identified. Among these, six candidates were selected for further stability analysis, including phonon band structure evaluations and electronic properties investigations, all of which exhibited phonon spectrum stability. To benchmark the performance of SCDM, a symmetry-unconstrained diffusion model was also evaluated via crystal structure prediction model. The results highlight that incorporating symmetry constraints enhances the effectiveness of generated 2D materials, making a contribution to the discovery of 2D materials through generative modeling.