【作者】 刘娟；

【导师】 汪长安；

【作者基本信息】 景德镇陶瓷大学 ， 材料学， 2023， 硕士

【摘要】 高熵陶瓷因其优异的性能和广阔的应用前景而备受关注,但其巨大的组分空间给高熵陶瓷领域的研究者带来了不小的挑战,繁琐的实验工艺严重阻碍着新型高熵材料体系的发现,高熵陶瓷领域迫切需要新的方式来进行探索。材料基因工程的提出,开创了基于数据驱动的新范式,这一理念的实现需要与人工智能技术相结合。本文基于“材料按需设计”的思想,建立了拟合优度R2达到0.9953的梯度提升决策树(Gradient Boosting Decision Tree,简称为GBDT)模型,并对70种不同高熵体系的熵形成能力EFA做出预测,进而对它们的单相形成能力做出判断。然后基于此模型设计了(TiVZrNbHf)Nx高熵陶瓷体系,通过热压烧结的方式成功制备出了具有单相的(TiVZrNbHf)Nx高熵陶瓷,并表征了该陶瓷的相组成、微观结构以及元素分布,测试了其力学性能。本文的主要研究内容与结果如下:(1)通过Lasso回归方法和皮尔逊相关系数法对建立的初始数据集进行特征值筛选,优化了初始数据集并找出了特征值与目标值之间存在的关系。建立Lasso回归模型,通过确定最佳λ值对特征进行压缩以达到筛选特征值的目的,筛选结果表明回归算法适用于本论文的研究。通过特征重要性排序探索了对EFA影响较大的特征值,并以此为依据设计了后续实验部分的高熵材料体系。(2)基于监督学习建立了四种机器学习模型:K最邻近模型、随机森林模型、支持向量机回归模型和梯度提升决策树模型,通过误差指标评估找出了性能表现最好的GBDT模型,并用于预测。在预测过程中,将GBDT模型用于预测含有70种不同高熵材料体系的熵形成能力EFA,在预测的结果中,拥有较大EFA的(AlTiCrMoW)Nx体系已有文献报道,由此验证了模型预测的准确能力。为探讨本文建立的GBDT模型同时具备良好的泛化能力,选取了生成焓和键长标准偏差作为目标值训练模型,从模型的拟合情况来看模型的R方均达到了 0.9以上,说明GBDT模型对两个新的目标值的拟合情况极好。(3)基于GBDT模型设计了(TiVZrNbHf)Nx高熵体系,并通过实验证明了该体系具备单相的形成能力。对制得的(TiVZrNbHf)Nx陶瓷进行物相分析和微观结构的观察等一系列表征,结果表明这一体系可以形成单一的岩盐结构相。进一步对样品进行力学测试,结果表明相比其单组分的氮化物,该陶瓷的硬度和断裂韧性均得到提升。更多还原

【Abstract】 High-entropy ceramics have attracted much attention due to their excellent properties and broad application prospects,however,their huge component space has brought great challenges to researchers in the field of high-entropy ceramics.The complicated experimental process seriously hinders the discovery of new high-entropy systems.The field of high-entropy ceramics urgently needs new ways to explore.The proposal of material genetic engineering has created a new paradigm based on data drive,and the realization of this concept needs to be combined with artificial intelligence technology.Based on the concept of "Material design on demand",this work established a Gradient Boosting Decision Tree(GBDT)model with a goodness of fit R2 of 0.9953,and predicted the entropy formation and single-phase forming abilities of 70 different high-entropy systems.After that,based on this model,a(TiVZrNbHf)Nx high-entropy system was designed,and a single-phase(TiVZrNbHf)Nx ceramic was successfully prepared by hotpressing sintering,and the phase composition,microstructure,element distribution and mechanical properties of the ceramic were characterized.The main research content and results of this paper are as follows:(1)Through filtering the features of the established initial dataset by the Lasso regression method and Pearson correlation coefficient method,the initial dataset was optimized to find out the relationship between features and target values.The Lasso regression model is established,and the features are compressed by determining the optimal λ value to achieve the purpose of screening features.The screening results show that the regression algorithm is suitable for the research of this paper.The features that have a greater impact on the EFA were explored through the ranking of feature importance.Based on this,the high-entropy ceramic systems of the follow-up experiment part were designed.(2)Based on supervised learning,four machine learning models were established:K Nearest Neighbor model,Random Forest model,Support Vector Regression model and Gradient Boosting Decision Tree model.Through the error index analysis,it is found that the GBDT model performs best,and then it is used for prediction.In the prediction process,the GBDT model is used to predict the EFA of 70 different high-entropy systems.Among the predicted results,the(AlTiCrMoW)Nx system has a larger EFA,and the single-phase(AlTiCrMoW)Nx system has been reported in the paper,which verifies the accuracy of the GBDT model.In order to explore that the GBDT model established in this paper has good generalization ability as well,the enthalpy of formation and the standard deviation of bond length is selected as the target value training model.From the model fitting situation,the R square of the model has reached more than 0.9,which shows that the GBDT model also fits the two new target values very well.(3)Based on the GBDT model,the(TiVZrNbHf)Nx high-entropy system was designed,and related experiments proved that the system has the ability to form a single phase.The phase analysis and microstructure observation and characterization of the obtained(TiVZrNbHf)Nx ceramics show that the system can form a single-phase rock-salt structure.The mechanical properties of the sample show that the ceramic exhibits improved hardness and fracture toughness compared to its one-component nitride.更多还原