标题： 单层二维材料数据库（ML2DDB）及其应用 显示英文标题

标题： Monolayer Two-dimensional Materials Database (ML2DDB) and Applications

摘要： 二维（2D）材料的发现，其特性经过设计，对于满足柔性电子、光电子、催化和能量存储等领域高性能应用的日益增长的需求至关重要。然而，当前的二维材料数据库受到规模和组成多样性的限制。在本研究中，我们引入了一种可扩展的主动学习工作流程，将深度神经网络与密度泛函理论（DFT）计算相结合，以高效地探索大量候选结构。这些结构通过物理启发的元素替代策略生成，实现了稳定二维材料的广泛和系统性发现。通过六次迭代筛选周期，我们建立了单层二维材料数据库（ML2DDB），其中包含242,546个DFT验证的稳定结构，比最大的已知二维材料数据库增加了一个数量级。特别是三元和四元化合物的数量增加了最多。将此数据库与生成扩散模型结合，我们展示了在特定化学和对称性约束下的有效结构生成。这项工作实现了二维材料数据扩展和应用的有机互联循环，为新材料的发现提供了一个新范式。 显示英文摘要

摘要： The discovery of two-dimensional (2D) materials with tailored properties is critical to meet the increasing demands of high-performance applications across flexible electronics, optoelectronics, catalysis, and energy storage. However, current 2D material databases are constrained by limited scale and compositional diversity. In this study, we introduce a scalable active learning workflow that integrates deep neural networks with density functional theory (DFT) calculations to efficiently explore a vast set of candidate structures. These structures are generated through physics-informed elemental substitution strategies, enabling broad and systematic discovery of stable 2D materials. Through six iterative screening cycles, we established the creation of the Monolayer 2D Materials Database (ML2DDB), which contains 242,546 DFT-validated stable structures-an order-of-magnitude increase over the largest known 2D materials databases. In particular, the number of ternary and quaternary compounds showed the most significant increase. Combining this database with a generative diffusion model, we demonstrated effective structure generation under specified chemistry and symmetry constraints. This work accomplished an organically interconnected loop of 2D material data expansion and application, which provides a new paradigm for the discovery of new materials.