xCr1–x體系RHEAs的實驗結果顯示，RF和ANN的預測精度更高，精準預測了11個SS和3個SS+IM合金的相組成。采用了SHAP（SHapley Additive exPlanations）模型來解釋精度最高的 ANN 模型，并研究每個特征對相形成的貢獻。5個特征的重要性順序是混合焓（ΔHmix）、原子尺寸差（δ）、價電子濃度（VEC）、混合熵（ΔSmix）和電負性差（Δχ），其中ΔHmix的平均SHAP值大約是Δχ的5倍，是ΔSmix的4倍。較大的ΔHmix、較小的δ和VEC可能有助于RHEA中固溶體的形成。"/>

最新色国产精品精品视频,中文字幕日韩一区二区不卡,亚洲有码转帖,夜夜躁日日躁狠狠久久av,中国凸偷窥xxxx自由视频

+高級檢索
基于可解釋機器學習模型的難熔高熵合金相預測
作者:
作者單位:

國防科技大學 空天科學學院 材料科學與工程系，湖南 長沙 410073

作者簡介:

通訊作者:

中圖分類號:

基金項目:

National Natural Science Foundation of China (Grant Nos. U20A20231 and 11972372) and College of Aerospace Science and Engineering Youth Talent Fund, National University of Defense Technology (Grant No. KY0505072209).


Interpretable Machine Learning Model-Based Phase Prediction for Refractory High-Entropy Alloys
Author:
Affiliation:

Department of Materials Science and Engineering, College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China

Fund Project:

National Natural Science Foundation of China (U20A20231, 11972372); College of Aerospace Science and Engineering Youth Talent Fund, National University of Defense Technology (KY0505072209)

  • 摘要
    • |
  • 圖/表
    • |
  • 訪問統計
    • |
  • 參考文獻
    • |
  • 相似文獻
    • |
  • 引證文獻
    • |
  • 資源附件
    • |
  • 文章評論
    摘要:

    采用k近鄰 （KNN）、支持向量機（SVM）、決策樹（DT）、隨機森林（RF）和人工神經網絡（ANN）5種機器學習（ML）方法對RHEAs中固溶體（SS）、混合固溶體和金屬間化合物（SS+IM）進行了分類和預測。選擇了5個輸入相預測參數作為特征以及139組RHEAs數據以訓練ML模型。結果表明，ANN模型的預測準確率最高，達到90.72%。9組新的四元和(TiVTa)xCr1–x體系RHEAs的實驗結果顯示，RF和ANN的預測精度更高，精準預測了11個SS和3個SS+IM合金的相組成。采用了SHAP（SHapley Additive exPlanations）模型來解釋精度最高的 ANN 模型，并研究每個特征對相形成的貢獻。5個特征的重要性順序是混合焓（ΔHmix）、原子尺寸差（δ）、價電子濃度（VEC）、混合熵（ΔSmix）和電負性差（Δχ），其中ΔHmix的平均SHAP值大約是Δχ的5倍，是ΔSmix的4倍。較大的ΔHmix、較小的δ和VEC可能有助于RHEA中固溶體的形成。

    Abstract:

    Five machine learning (ML) approaches, i.e. K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) were used to classify and to predict the combination of phases, i.e. solid solutions (SS) and mixed solid solution and intermetallic (SS+IM) in refractory high-entropy alloys (RHEAs). Five input characteristic phase predicting parameters and 139 RHEAs were selected to train these models. Results show that ANN model has the highest accuracy of 90.72%. Experimental results of 9 quaternary and (TiVTa)xCr1–x RHEAs verify the accuracy of prediction and indicate that RF and ANN can predict more accurately, successfully predicting 11 SS and 3 SS+IM. SHAP (SHapley Additive exPlanations) model was used to interpret the ANN model which exhibits the highest accuracy and to investigate the contribution of each feature to phase formation. The order of importance of five features is enthalpy of mixing (ΔHmix), atomic size difference (δ), valence electron concentration (VEC), entropy of mixing (ΔSmix), and electronegativity difference (Δχ), where the mean SHAP value of ΔHmix is approximately 5 times higher than that of ?χ and 4 times higher than that of ΔSmix. Less negative ΔHmix, smaller δ and VEC may contribute to the formation of SS in RHEAs.

    參考文獻
    相似文獻
    引證文獻
引用本文

趙鳳媛,葉益聰,張周然,李亞豪,王潔,唐宇,李順,白書欣.基于可解釋機器學習模型的難熔高熵合金相預測[J].稀有金屬材料與工程,2023,52(4):1192~1200.[Zhao Fengyuan, Ye Yicong, Zhang Zhouran, Li Yahao, Wang Jie, Tang Yu, Li Shun, Bai Shuxin. Interpretable Machine Learning Model-Based Phase Prediction for Refractory High-Entropy Alloys[J]. Rare Metal Materials and Engineering,2023,52(4):1192~1200.]
DOI：10.12442/j. issn.1002-185X.20220750

復制
文章指標
  • 點擊次數:
  • 下載次數:
  • HTML閱讀次數:
  • 引用次數:
歷史
  • 收稿日期:2022-09-20
  • 最后修改日期:2023-03-23
  • 錄用日期:2022-12-23
  • 在線發(fā)布日期: 2023-04-28
  • 出版日期: 2023-04-25