Predicting the properties of materials prior to their synthesis is of great importance in materials science. Magnetic and superconducting materials exhibit a number of unique properties that make them useful in a wide variety of applications, including solid oxide fuel cells, solid-state refrigerants, photon detectors and metrology devices. In all these applications, phase transitions play an important role in determining the feasibility of the materials in question. Here, we present a pipeline for fully integrating data extracted from the scientific literature into machine-learning tools for property prediction and materials discovery. Using advanced natural language processing (NLP) and machine-learning techniques, we successfully reconstruct the phase diagrams of well-known magnetic and superconducting compounds, and demonstrate that it is possible to predict the phase-transition temperatures of compounds not present in the database. We provide the tool as an online open-source platform, forming the basis for further research into magnetic and superconducting materials discovery for potential device applications.

在材料合成之前预测材料的特性在材料科学中非常重要。磁性和超导材料具有许多独特的性能，使其可用于各种应用，包括固体氧化物燃料电池，固态制冷剂，光子探测器和计量设备。在所有这些应用中，相变在确定有关材料的可行性方面起着重要作用。在这里，我们提出了一个管道，用于将从科学文献中提取的数据完全集成到用于特性预测和材料发现的机器学习工具中。使用先进的自然语言处理（NLP）和机器学习技术，我们成功地重建了著名的磁性和超导化合物的相图，并证明可以预测数据库中不存在的化合物的相变温度。我们提供了作为在线开源平台的工具，为进一步研究潜在设备应用中的磁性和超导材料发现奠定了基础。