Databases for two-dimensional materials host numerous ferromagnetic materials without the vital information of Curie temperature since its calculation involves a manually intensive complex process. In this work, we develop a fully automated, hardware-accelerated, dynamic-translation based computer code, which performs first principles-based computations followed by Heisenberg model-based Monte Carlo simulations to estimate the Curie temperature from the crystal structure. We employ this code to conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end machine learning model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model. Such material informatics, which agrees well with recent experiments, is expected to foster practical applications of two-dimensional magnetism.

二维材料的数据库中包含许多铁磁材料，而没有居里温度的重要信息，因为其计算涉及人工密集的复杂过程。在这项工作中，我们开发了一种全自动的，基于硬件加速的，基于动态翻译的计算机代码，该代码执行基于第一原理的计算，然后执行基于Heisenberg模型的蒙特卡洛模拟，以根据晶体结构估算居里温度。我们使用此代码对数据库中的786种材料进行高通量扫描，以发现居里点超过400 K的26种材料。对于快速数据挖掘，我们进一步使用这些结果来开发端到端机器学习模型通过详尽搜索模型空间以及超参数来获得具有广义化学特征的特征。我们使用此数据驱动模型从不同来源发现了更多的居里点材料。这种材料信息学与最近的实验非常吻合，有望促进二维磁性的实际应用。