A hybrid prediction frame for HEAs based on empirical knowledge and machine learning
A hybrid prediction frame for HEAs based on empirical knowledge and machine learning
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DOI：
10.1016/j.actamat.2022.117742
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发表时间：
2022-02-23
期刊：
影响因子：
9.4
通讯作者：
Liu, Weiwei
中科院分区：
文献类型：
--
作者：
Hou, Shuai;Sun, Mengyue;Liu, Weiwei
相的形成对高熵合金的性能起着至关重要的作用。如果能够准确地预测HEAS的相态，可以大大减少实验次数，大大加快材料设计的进程。机器学习方法已被成功地广泛应用于混合电器的相态预测。然而，单一的机器学习(ML)算法的精度并不理想，不同的ML算法可能会预测不同的结果。这些问题阻碍了ML在材料设计中的应用。本文将机器学习和经验知识相结合，提出了一种混合的HEAS相位预测框架。首先，为了解决不同算法预测样本的预测阶段不一致的问题，采用Dempster-Shafer(DS)证据理论对不同算法之间预测阶段的不一致进行融合，提供可信度最高的融合预测阶段。其次，提出了一种基于改进DS证据理论的高精度冲突消解模型。最后，将经验知识准则与冲突消解模型相结合，提高了混合预测框架的效率和精度。收集了426个不同的HEAs样本，包括180个五分位数、189个四分位数和57个七位数，以验证所提出的方法的有效性。实验结果表明，与单一的最大似然算法相比，混合预测框架具有更高的准确率和更好的性能。关键词：混合模型；高熵合金；相预测；DS证据理论(C)2022材料学报。爱思唯尔出版。版权所有。
Phase formation plays key role in the properties of high-entropy alloys (HEAs). If the phases of HEAs can be accurately predicted, the number of experiments can be greatly reduced, and the process of material design can be greatly accelerated. Machine-learning methods have been successfully and widely applied to predict the phases of HEAs. However, the accuracy of a single machine-learning (ML) algorithm is not ideal and different ML algorithms may predict different results. These issues hinder the application of ML in material design. In this paper, a hybrid frame for HEAs phase prediction, which combines machine learning and empirical knowledge, is proposed. First, for the purpose of solving the problem that a sample may be predicted as inconsistent prediction phases by different algorithms, the Dempster-Shafer (DS) evidence theory is adopted to fuse the inconsistent of the predicted phases among different algorithms, and provide a fusion prediction phase with the highest credibility. Second, a conflict-resolution model with high accuracy based on the improved DS evidence theory is proposed. Last, the empirical knowledge criterion is combined with the conflict-resolution model to improve the efficiency and accuracy of the hybrid prediction frame. The 426 different HEAs samples consisting of 180 quinaries, 189 senaries, and 57 septenaries were collected to validate against the effectiveness of the proposed methods. The experimental results demonstrate the hybrid prediction frame achieves higher accuracy and better performance than single ML algorithm. Keywords: Hybrid model; High-entropy alloys; Phase prediction; DS evidence theory (c) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.