We discover the chemical composition of over 1000 materials that are likely to exhibit layered and 2D phases but have yet to be synthesized. This includes two materials our calculations indicate can exist in distinct structures with different band gaps, expanding the short list of 2D phase-change materials. Whereas databases of over 1000 layered materials have been reported, we provide the first full database of materials that are likely layered but are yet to be synthesized, providing a roadmap for the synthesis community. We accomplish this by combining physics with machine learning on experimentally obtained data and verify a subset of candidates using density functional theory. We find that our model performs five times better than practitioners in the field at identifying layered materials and is comparable to or better than professional solid-state chemists. Finally, we find that semisupervised learning can offer benefits for materials design where labels for some of the materials are unknown.

中文翻译：

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揭示具有超人预测能力的未知分层材料的光谱

我们发现了1000多种材料的化学成分，这些材料可能会显示出层状和2D相，但尚未合成。这包括我们计算得出的两种材料，它们可以存在于具有不同带隙的不同结构中，从而扩大了二维相变材料的简短清单。尽管已报告了1000多种分层材料的数据库，但我们提供了第一个完整的可能分层但尚未合成的材料的完整数据库，从而为合成社区提供了路线图。我们通过将物理与机器学习相结合来完成实验，以获取实验数据，并使用密度泛函理论验证候选子集。我们发现，在识别分层材料方面，我们的模型的性能比同领域的从业人员高五倍，并且可以与专业的固态化学家相媲美或更好。最后，我们发现半监督学习可以为某些材料的标签未知的材料设计提供好处。