Two-dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. Although several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains challenging. Herein, we propose a deep learning generative model for composition generation combined with a random forest-based 2D materials classifier to discover new hypothetical 2D materials. Furthermore, a template-based element substitution structure prediction approach is developed to predict the crystal structures of a subset of the newly predicted hypothetical formulas, which allows us to confirm their structure stability using DFT calculations. So far, we have discovered 267 489 new potential 2D materials compositions, where 1485 probability scores are more then 0.95. Among them, we have predicted 101 crystal structures and confirmed 92 2D/layered materials by DFT formation energy calculation. Our results show that generative machine learning models provide an effective way to explore the vast chemical design space for new 2D materials discovery.

中文翻译：

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使用深度学习生成模型计算发现新的 2D 材料

二维 (2D) 材料因其独特的光电特性而成为具有许多应用的有前途的功能材料，例如半导体和光伏。尽管在现有材料数据库中筛选了数千种 2D 材料，但发现新的 2D 材料仍然具有挑战性。在此，我们提出了一种用于成分生成的深度学习生成模型，结合基于随机森林的 2D 材料分类器，以发现新的假设 2D 材料。此外，开发了一种基于模板的元素取代结构预测方法来预测新预测的假设公式子集的晶体结构，这使我们能够使用 DFT 计算确认它们的结构稳定性。到目前为止，我们已经发现了 267 489 种新的潜在二维材料组合物，其中 1485 个概率分数大于 0.95。其中，我们预测了101种晶体结构，并通过DFT形成能计算确认了92种2D/层状材料。我们的结果表明，生成式机器学习模型为探索用于新 2D 材料发现的广阔化学设计空间提供了一种有效的方法。