阅读说明：本技术 基于机器学习模型设计高热稳定性铁基软磁非晶合金 (High-thermal-stability iron-based soft magnetic amorphous alloy designed based on machine learning model ) 是由 刘雄军 吕志超 王辉 吴渊 蒋虽合 吕昭平 于 2020-11-27 设计创作，主要内容包括：本发明公开了一种利用可解释性的XGBoost机器学习算法设计高热稳定性铁基软磁非晶合金的方法,具体的步骤包括：(1)建立铁基非晶合金数据集；(2)数据集预处理；(3)通过机器学习建立预测模型,进行重要特征提取；(4)挖掘重要特征背后的物理意义,建立具有高准确度的合金设计准则；(5)合金设计准则的实验验证。本发明基于现有铁基非晶合金软磁性能和热稳定性的实验数据,利用机器学习模型可同时预测未知铁基软磁非晶合金的饱和磁感应强度(B-s)和初始晶化温度(T-x),具有工作量小、可解释性强、精度高、可靠性高、可操作性强等优点,可应用于设计不同体系铁基软磁非晶合金,显著提高了新型高性能软磁合金开发的效率、降低研发成本。(The invention discloses a method for designing a high-thermal-stability iron-based soft magnetic amorphous alloy by using an interpretable XGboost machine learning algorithm, which comprises the following specific steps of: (1) establishing an iron-based amorphous alloy data set; (2) preprocessing a data set; (3) establishing a prediction model through machine learning, and extracting important features; (4) digging the physical significance behind the important features and establishing an alloy design criterion with high accuracy; (5) and (5) experimental verification of alloy design criteria. The invention is based on the experimental data of soft magnetic property and thermal stability of the existing Fe-based amorphous alloy, and can simultaneously use a machine learning modelPredicting the saturation induction density (B) of unknown Fe-based soft magnetic amorphous alloy s ) And initial crystallization temperature (T) x ) The method has the advantages of small workload, strong interpretability, high precision, high reliability, strong operability and the like, can be applied to design iron-based soft magnetic amorphous alloys of different systems, obviously improves the development efficiency of novel high-performance soft magnetic alloys, and reduces the research and development cost.)