The unique electronic and mechanical properties of two-dimensional (2D) materials make them promising next-generation candidates for a variety of applications. Large-scale searches for high-performing 2D materials are limited to calculating descriptors with computationally demanding first-principles density functional theory. In this work, we alleviate this issue by extending and generalizing crystal graph convolutional neural networks to systems with planar periodicity and train an ensemble of models to predict thermodynamic, mechanical and electronic properties. We carry out a screening of nearly 45,000 structures for two separate applications: mechanical strength and photovoltaics. By collecting statistics of the screened candidates, we investigate structural and compositional design principles that impact the properties of the structures surveyed. Our approach recovers some well-accepted design rules: hybrid organic-inorganic perovskites with lead and tin tend to be good can...

中文翻译：

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机器学习使发现二维材料的依赖于应用程序的设计原理成为可能

二维（2D）材料的独特电子和机械性能使它们成为各种应用程序的有希望的下一代候选产品。对高性能2D材料的大规模搜索仅限于使用对计算有严格要求的第一原理密度泛函理论来计算描述符。在这项工作中，我们通过将晶体图卷积神经网络扩展和泛化为具有平面周期性的系统来缓解此问题，并训练了一组模型来预测热力学，机械和电子性质。我们对两种应用分别进行了将近45,000种结构的筛选：机械强度和光伏。通过收集筛选过的候选人的统计数据，我们研究了影响所调查结构特性的结构和成分设计原则。我们的方法恢复了一些公认的设计规则：铅和锡的杂化有机-无机钙钛矿往往是好的。