论文标题
基于机器学习的硬磁质量密度模型14：2：1使用基于化学成分的特征
Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features
论文作者
论文摘要
基于FE14ND2B的永久磁铁是由于其无与伦比的高能量产品（520 kJ/m3）而在技术上寻求的。对于这种不同组合物的14：2：1阶段，通常由于缺乏质量密度来确定测得的磁矩的磁化。我们使用基于化学成分的特征（代表33个元素）和晶格参数（LP）特征提供了14：2：1阶段的机器学习（ML）质量密度模型。培训和测试的数据集包含190个阶段（177个构图不同），其文献报道了密度和LP。借助仅具有组成特征的ML模型，我们在看不见的测试数据集中达到了低均值释放率为0.51％。
The Fe14Nd2B-based permanent magnets are technologically sought-after for energy conversion due to their unparalleled high energy product (520 kJ/m3). For such 14:2:1 phases of different compositions, determining the magnetization from the measured magnetic moment is often bottlenecked by lack of mass density. We present a machine learning (ML) mass density model for 14:2:1 phases using chemical composition-based features (representing 33 elements) and optionally lattice parameter (LP) features. The datasets for training and testing contain 190 phases (177 compositionally different) with their literature reported densities and LP. With an ML model with merely compositional features, we achieved a low mean-absolute-error of 0.51% on an unseen test-dataset.
