AI辅助识别磁有序和斯格明子
AI-Assisted Identification of Magnetic Orders and Skyrmions
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中文总结 AI 辅助
本研究开发了一个机器学习框架，利用Materials Project数据通过LightGBM分类和残差多层感知器回归，实现二维材料磁有序识别（准确率93.6%）和磁化强度预测（MAE 0.686 μB/化学式单位），并辅助筛选反铁磁和拓扑自旋电子学候选材料。
中文摘要 AI 辅助
二维（2D）材料中的奇异磁有序正引起人们对节能自旋电子学应用的极大兴趣，然而实现稳健的高温范德华反铁磁体和拓扑磁态仍然具有挑战性。在这项工作中，我们开发了一个机器学习框架，利用从Materials Project获得的结构、成分和电子信息来识别磁有序并预测磁化强度。针对两个互补任务构建了固定长度描述符：铁磁（FM）与反铁磁（AFM）分类以及定量磁化强度预测。使用LightGBM模型对磁有序进行分类，该模型基于结构衍生描述符训练，未显式包含磁性描述符。采用按化学体系分组的五折交叉验证以减少化学泄漏，同时进行超参数和分类阈值优化。在独立的测试集上，分类器在测试集中达到了93.6%的平衡准确率。磁化强度使用残差多层感知器进行预测，该感知器结合了成分、结构、电子和任务特定的初始状态描述符。采用对数目标变换和稳健加权损失以适应广泛的磁化强度分布，同时将三个独立训练的模型组合成最终集成模型。该模型实现了0.686 μB/化学式单位的平均绝对误差。对于选定的磁性候选材料，进一步使用模拟磁成像和相位重建分析，通过归一化磁化强度分布和拓扑电荷来研究磁化纹理和类斯格明子特征。该框架为筛选二维磁性材料以及优先考虑反铁磁和拓扑自旋电子学应用的候选材料提供了一种高效方法。
英文摘要
Exotic magnetic orders in two-dimensional (2D) materials are attracting huge interest for energy-efficient spintronic applications, yet realizing robust high-temperature van der Waals antiferromagnets and topological magnetic states remains challenging. In this work, we develop a machine-learning framework for identifying magnetic orders and predicting magnetization using structural, compositional, and electronic information derived from the Materials Project. Fixed-length descriptors are constructed for two complementary tasks: ferromagnetic (FM) versus antiferromagnetic (AFM) classification and quantitative magnetization prediction. Magnetic order is classified using a LightGBM model trained on structure-derived descriptors without explicitly including magnetic descriptors. Five-fold cross-validation grouped by chemical system is used to reduce chemical leakage, together with hyperparameter and classification-threshold optimization. On an isolated test set, the classifier achieves a balanced accuracy of 93.6% in the testing set. Magnetization is predicted using a residual multilayer perceptron with compositional, structural, electronic, and task-specific initial-state descriptors. A logarithmic target transformation and robust weighted loss are used to account for the broad magnetization distribution, while three independently trained models are combined into a final ensemble. The model achieves a mean absolute error of 0.686 μB/formula unit. For selected magnetic candidates, simulated magnetic imaging and phase-reconstruction analysis are further used to investigate magnetization textures and skyrmion-like features through normalized magnetization profiles and topological charge. This framework provides an efficient approach for screening 2D magnetic materials and prioritizing candidates for antiferromagnetic and topological spintronic applications.
发表机构
- University of Florida（佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。
