Efficient algorithms to generate candidate crystal structures with good stability properties can play a key role in data-driven materials discovery. Here we show that a crystal diffusion variational autoencoder (CDVAE) is capable of generating two-dimensional (2D) materials of high chemical and structural diversity and formation energies mirroring the training structures. Specifically, we train the CDVAE on 2615 2D materials with energy above the convex hull $\Delta H_{\mathrm{hull}}< 0.3$ eV/atom, and generate 5003 materials that we relax using density functional theory (DFT). We also generate 14192 new crystals by systematic element substitution of the training structures. We find that the generative model and lattice decoration approach are complementary and yield materials with similar stability properties but very different crystal structures and chemical compositions. In total we find 11630 predicted new 2D materials, where 8599 of these have $\Delta H_{\mathrm{hull}}< 0.3$ eV/atom as the seed structures, while 2004 are within 50 meV of the convex hull and could potentially be synthesized. The relaxed atomic structures of all the materials are available in the open Computational 2D Materials Database (C2DB). Our work establishes the CDVAE as an efficient and reliable crystal generation machine, and significantly expands the space of 2D materials.
翻译:生成具有良好稳定性特性的候选晶体结构的有效算法可以在数据驱动材料的发现中发挥关键作用。 在这里, 我们显示晶体扩散变异自动编码器(CDVAE)能够生成高化学和结构多样性的二维(2D)材料, 以及反映培训结构的成型能量。 具体地说, 我们用能量高于锥形壳的2615 2D材料对 CDVAE 进行2615 2D 材料的培训 $\ Delta H ⁇ mathrm{hull} 0. 0. 3 eV/ 原子, 并生成5003 材料, 使用密度功能理论(DFT)来放松。 我们还通过系统替换培训结构生成了14192个新的晶体。 我们发现, 基因模型和衬垫装饰法是互补材料的,产生类似稳定性但晶体结构与化学结构非常不同。 我们总共发现了11630种预测的2DDM材料, 其中8 599美元作为种子结构, 0.3 eV/Datoum 0.3 。 2004年, 我们所有材料都在50MVF 内, 并且可以大量合成的硬质材料。 我们的软化的CD- DVADBIDBs 建立一个可加的磁制制成的硬化的磁制成的2 。