Liu Dan, Liu Zhixin, Zhang JinE, Yin Yinong, Xi Jianfeng, Wang Lichen, Xiong JieFu, Zhang Ming, Zhao Tongyun, Jin Jiaying, Hu Fengxia, Sun Jirong, Shen Jun, Shen Baogen
Department of Physics, School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, P. R. China.
School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China.
Research (Wash D C). 2023;6:0082. doi: 10.34133/research.0082. Epub 2023 Mar 15.
The discovery and study of skyrmion materials play an important role in basic frontier physics research and future information technology. The database of 196 materials, including 64 skyrmions, was established and predicted based on machine learning. A variety of intrinsic features are classified to optimize the model, and more than a dozen methods had been used to estimate the existence of skyrmion in magnetic materials, such as support vector machines, -nearest neighbor, and ensembles of trees. It is found that magnetic materials can be more accurately divided into skyrmion and non-skyrmion classes by using the classification of electronic layer. Note that the rare earths are the key elements affecting the production of skyrmion. The accuracy and reliability of random undersampling bagged trees were 87.5% and 0.89, respectively, which have the potential to build a reliable machine learning model from small data. The existence of skyrmions in LaBaMnO is predicted by the trained model and verified by micromagnetic theory and experiments.
斯格明子材料的发现与研究在基础前沿物理研究和未来信息技术中发挥着重要作用。基于机器学习建立并预测了包含64种斯格明子的196种材料的数据库。对多种内在特征进行分类以优化模型，并且已经使用了十几种方法来估计磁性材料中斯格明子的存在，如支持向量机、最近邻法和树集成法。研究发现，利用电子层分类可以更准确地将磁性材料分为斯格明子类和非斯格明子类。注意，稀土是影响斯格明子产生的关键元素。随机欠采样袋装树的准确率和可靠性分别为87.5%和0.89，具有从小数据构建可靠机器学习模型的潜力。通过训练模型预测了LaBaMnO中斯格明子的存在，并通过微磁学理论和实验进行了验证。
