增强磁热材料发现：一种利用大语言模型自动生成数据库的机器学习方法
Enhancing Magnetocaloric Material Discovery: A Machine Learning Approach Using an Autogenerated Database by Large Language Models
AI总结：
针对10–100 K磁制冷材料缺口，本文用大语言模型构建6000余条材料数据库，训练成分驱动的磁热性质预测模型，并经从头算自旋动力学验证，发现11种候选材料。
摘要：
基于磁热效应的磁制冷是一项很有前景的固态制冷技术，可在不同温度范围内广泛应用。以往研究主要集中在近室温（300 K）和低温（<10 K）区间，而氢液化等重要应用需要适用于10 K至100 K中间温度的高效磁性制冷剂。为在该区间高效使用，需要发现具有匹配居里温度的新型磁热材料，但传统实验方法通常耗时且昂贵。在此，我们报告了一条计算材料发现流程，其基础是一个包含6000多条记录的材料数据库；该数据库通过使用大语言模型从文献中提取已报道的材料性质而自动生成。随后，我们利用该数据库训练机器学习模型，使其能够根据材料的化学成分高效预测材料的磁热性质。我们进一步使用从头算原子自旋动力学模拟验证所预测化合物的磁热性质，从而闭合计算材料发现的循环。借助这一方法，我们为目标温度区间确定了11种新的有前景的磁热材料。本研究展示了将大语言模型、机器学习和从头算模拟相结合以高效发现新型功能材料的潜力。
英文摘要：
Magnetic cooling based on the magnetocaloric effect is a promising solid-state refrigeration technology for a wide range of applications in different temperature ranges. Previous studies have mostly focused on near room temperature (300 K) and cryogenic temperature (< 10 K) ranges, while important applications such as hydrogen liquefaction call for efficient magnetic refrigerants for the intermediate temperature 10K to 100 K. For efficient use in this range, new magnetocaloric materials with matching Curie temperatures need to be discovered, while conventional experimental approaches are typically time-consuming and expensive. Here, we report a computational material discovery pipeline based on a materials database containing more than 6000 entries auto-generated by extracting reported material properties from literature using a large language model. We then use this database to train a machine learning model that can efficiently predict magnetocaloric properties of materials based on their chemical composition. We further verify the magnetocaloric properties of predicted compounds using ab initio atomistic spin dynamics simulations to close the loop for computational material discovery. Using this approach, we identify 11 new promising magnetocaloric materials for the target temperature range. Our work demonstrates the potential of combining large language models, machine learning, and ab initio simulations to efficiently discover new functional materials.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
