Please wait a minute...
材料导报  2022, Vol. 36 Issue (Z1): 21120179-5    
  金属与金属基复合材料 |
基于机器学习的Laves相生成焓预测研究
赵巍, 花福安, 李建平
东北大学轧制技术及连轧自动化国家重点实验室,沈阳 110819
Study on Prediction of Formation Enthalpy of Laves Phase Alloys Based on Machine Learning
ZHAO Wei, HUA Fu'an, LI Jianping
State Key Laboratory of Rolling Technology and Continuous Rolling Automation, Northeastern University, Shenyang 110819, China
下载:  全 文 ( PDF ) ( 4420KB ) 
输出:  BibTeX | EndNote (RIS)      
摘要 Laves相合金新材料的合成设计离不开生成焓的测定。近几年来,为解决生成焓测定低效的问题,机器学习预测生成焓的新模式得到了广泛的关注。本工作通过三种特征集和七种回归算法,构建了21个机器学习回归模型。基于从OQMD数据库收集的Laves相合金生成焓的实验值数据集,系统地比较了算法选择和特征选择对模型综合性能的影响。结果表明,RF模型综合性能最高,高于Miedema半经验模型的预测精度,实现了Laves相合金生成焓的快速预测。成分特征对于生成焓的影响程度远大于结构特征,其中,电子因素比尺寸因素的影响程度更大。将Laves相生成焓的DFT计算值加入到实验值数据集,组成大样本数据集,基于此建立的ML模型精度有大幅提升。该方法可用于新设计的Laves相合金材料的快速初筛,从而推动新型Laves相合金的研发进度。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
赵巍
花福安
李建平
关键词:  Laves相  机器学习  生成焓  回归模型    
Abstract: The determination of formation enthalpy is indispensable to the synthetic design of new Laves phase alloy materials. In recent years, in order to solve the problem of low efficiency in the determination of enthalpy of formation, a new model for predicting enthalpy of formation based on machine learning has received extensive attention. In this work, 21 machine learning regression models were constructed by three feature sets and seven regression algorithms. Based on the data set of formation enthalpy of Laves phase alloys collected from OQMD database, the effects of algorithm selection and feature selection on the overall performance of the model were systematically compared. The results show that the RF model has the highest comprehensive performance, which is higher than the prediction accuracy of Miedema semi-empirical model, and can quickly predict the formation enthalpy of Laves phase alloy. The influence of component characteristics on formation enthalpy is much greater than that of structure characteristics, and the influence of electron factors is greater than that of size factors. The DFT calculated value of formation enthalpy of Laves phase was added to the experimental data set to form a large sample data set, on which the accuracy of the ML model established was greatly improved. This method can be used for rapid preliminary screening of newly designed Laves phase alloy materials, thus improving the development progress of new Laves phase alloy.
Key words:  Laves phase    machine learning    formation enthalpy    regression model
出版日期:  2022-06-05      发布日期:  2022-06-08
ZTFLH:  O642  
通讯作者:  huafa@ral.neu.edu.cn   
作者简介:  赵巍,东北大学材料科学与工程学院硕士研究生,在花福安教授的指导下进行研究。目前主要研究领域为机器学习在材料性质预测上的应用。
花福安,东北大学材料科学与工程学院教授、硕士研究生导师。2004年获得中国科学院金属研究所金属加工研究所材料加工工程博士学位。主要研究方向包括材料加工、材料加工的建模和仿真、材料加工设备的研发以及材料科学中的机器学习应用等。在国内外学术期刊上发表论文20多篇,授权国家发明专利10余项,曾获国家科技进步二等奖。
引用本文:    
赵巍, 花福安, 李建平. 基于机器学习的Laves相生成焓预测研究[J]. 材料导报, 2022, 36(Z1): 21120179-5.
ZHAO Wei, HUA Fu'an, LI Jianping. Study on Prediction of Formation Enthalpy of Laves Phase Alloys Based on Machine Learning. Materials Reports, 2022, 36(Z1): 21120179-5.
链接本文:  
http://www.mater-rep.com/CN/  或          http://www.mater-rep.com/CN/Y2022/V36/IZ1/21120179
1 Guo Q T, Kleppa O J. Journal of Alloys and Compounds, 2001, 321(2), 169.
2 Kim G, Meschel S V, Nash P, et al. Scientific Data, 2017, 4, 162.
3 Zhang R F, Sheng S H, Liu B X. Chemical Physics Letters, 2007, 442, 511.
4 吴春峰, 李慧改, 郑少波, 等.上海金属, 2011, 33(4), 1.
5 周志敏, 孙艳蕊.东北大学学报(自然科学版), 2013, 34(11), 1585.
6 Kirklin S, Saal J E, Meredig B, et al. npj Computational Materials, 2015, 1, 15010.
7 Saal J E, Kirklin S, Aykol M, et al. Journal of Metals, Materials and Minerals, 2013, 65(11), 1501.
8 Ong S P, Cholia S, Ceder G, et al. Computational Materials Science, 2015, 97, 209.
9 Jain A, Ong S P, Hautier G, et al. APL Materials, 2013, 1, 1.
10 Ward L, Agrawal A, Choudhary A, et al. npj Computational Materials, 2016, 2, 16028.
11 Ubaru S, Midlar A, Saad Y, et al. Physical Review B, 2017, 95, 21.
12 Sun S P, Yi D Q, Jiang Y, et al. Materials Chemistry and Physics, 2011, 126(3), 632.
13 李健聪, 王泰然, 舒武, 等.中国科学技术大学学报, 2020, 50(6), 844.
14 Eremin R A, Zolotarev P, Leisegang T, et al. AIP Conference Procee-dings, 2019, 2163(1), 020003.
15 Edirisuriya M D, Joshi R P, Kumar N, et al. ACS Applied Materials and Interfaces, 2020, 12, 26.
16 徐雅斌, 孙胜杰, 武装.含能材料, 2021, 29(1), 20.
17 Jha D, Choudhary K, Tavazza F, et al. Nature Communications, 2020, 11, 1.
18 Zheng X, Peng Z, Zhang R. Chemical Science, 2018, 9, 44.
19 Lotfi S, Zhang Z, Viswanathan G, et al. Matter, 2020, 3, 1.
20 Liu Y, Zhao T, Ju W, et al. Journal of Materiomics, 2017, 3, 3.
21 Villars P, Brandenburg K, Berndt M, et al. Journal of Alloys and Compounds, 2001, 317, 26.
22 鲁世强, 黄伯云, 贺跃辉, 等.材料工程, 2003, 5, 43.
23 鲁世强, 黄伯云, 贺跃辉, 等.材料导报, 2003(1), 11.
24 Kandavel M, Bhat V, Rougier A, et al. International Journal of Hydrogen Energy, 2008, 33, 14.
25 Kandavel M, Ramaprabhu S. International Journal of Hydrogen Energy, 2007, 32(5), 620.
26 Zhu J H, Liu C T, Pike L M, et al. Intermetallics, 2002, 10(6), 579.
27 Teixeira A L, Leal J P, Falcao A O. Journal of Cheminformatics, 2013, 5(1), 9.
28 Seko A, Hayashi H, Nakayama K, et al. Physical Review B, 2017, 95, 14.
29 Schutt K T, Glawe H, Brockherde F, et al. Physical Review B, 2013, 89(20), 163.
30 Ward L, Liu R, Krishna A, et al. Physical Review B, 2017, 96, 2.
31 Ward L, Dunn A, Faghaninia A, et al. Computational Materials Science, 2018, 152, 60.
[1] 杜青铉, 张宇航, 孙伟豪, 刘蕊, 庄尧量, 夏军武. 基于混合模型的煤矸石透水混凝土透水系数预测[J]. 材料导报, 2022, 36(Z1): 22040077-5.
[2] 侯腾跃, 孙炎辉, 孙舒鹏, 肖瑛, 郑雁公, 王兢, 杜海英, 吴隽新. 机器学习在材料结构与性能预测中的应用综述[J]. 材料导报, 2022, 36(6): 20080205-12.
[3] 宋庆功, 常斌斌, 董珊珊, 顾威风, 康建海, 王明超, 刘志锋. 机器学习及其在材料研发中的作用[J]. 材料导报, 2022, 36(1): 20080139-7.
[4] 张猛, 花福安, 赵巍. 基于机器学习的高熵合金生成相预测研究[J]. 材料导报, 2021, 35(Z1): 331-335.
[5] 郑玉杰, 梁鑫斌, 张起, 孙文博, 施童超, 杜鹃, 孙宽. 基于分子指纹及机器学习回归模型的有机光伏材料效率预测[J]. 材料导报, 2021, 35(8): 8207-8212.
[6] 万杨杰, 钱晓英, 曾迎, 孙可欣, 张英波, 权高峰. Al-Zn-Mg合金的相图计算及电子结构与力学性能的第一性原理计算[J]. 材料导报, 2021, 35(2): 2139-2144.
[7] 米晓希, 汤爱涛, 朱雨晨, 康靓, 潘复生. 机器学习技术在材料科学领域中的应用进展[J]. 材料导报, 2021, 35(15): 15115-15124.
[8] 牛程程, 李少波, 胡建军, 但雅波, 曹卓, 李想. 机器学习在材料信息学中的应用综述[J]. 材料导报, 2020, 34(23): 23100-23108.
[9] 康靓, 米晓希, 王海莲, 吴璐, 孙凯, 汤爱涛. 人工神经网络在材料科学中的研究进展[J]. 材料导报, 2020, 34(21): 21172-21179.
[10] 侯永强, 尹升华, 曹永, 戴超群. 基于RSM-BBD的混合骨料胶结充填体强度增长规律分析[J]. 材料导报, 2020, 34(14): 14063-14069.
[11] 易金权, 曾凯, 邢保英, 冯煜阳, 翟停停. DP780高强钢胶接点焊的多元非线性回归模型[J]. 材料导报, 2020, 34(11): 11071-11075.
[12] 巩位,余红发,麻海燕,达波. 全珊瑚海水混凝土配合比设计及评价方法[J]. 材料导报, 2019, 33(22): 3732-3737.
[13] 闫存富, 李淑娟. 陶瓷材料低温挤压自由成形工艺液相迁移研究[J]. 《材料导报》期刊社, 2018, 32(4): 636-640.
[14] 丁雨田,豆正义,高钰璧,高 鑫,李海峰,刘德学. 原位观察不同冷却速率下GH3625合金的凝固过程[J]. 《材料导报》期刊社, 2017, 31(24): 150-155.
[1] Wei ZHOU, Xixi WANG, Yinlong ZHU, Jie DAI, Yanping ZHU, Zongping SHAO. A Complete Review of Cobalt-based Electrocatalysts Applying to Metal-Air Batteries and Intermediate-Low Temperature Solid Oxide Fuel Cells[J]. Materials Reports, 2018, 32(3): 337 -356 .
[2] Dongyong SI, Guangxu HUANG, Chuanxiang ZHANG, Baolin XING, Zehua CHEN, Liwei CHEN, Haoran ZHANG. Preparation and Electrochemical Performance of Humic Acid-based Graphitized Materials[J]. Materials Reports, 2018, 32(3): 368 -372 .
[3] Yunzi LIU,Wei ZHANG,Zhanyong SONG. Technological Advances in Preparation and Posterior Treatment of Metal Nanoparticles-based Conductive Inks[J]. Materials Reports, 2018, 32(3): 391 -397 .
[4] Bingwei LUO,Dabo LIU,Fei LUO,Ye TIAN,Dongsheng CHEN,Haitao ZHOU. Research on the Two Typical Infrared Detection Materials Serving at Low Temperatures: a Review[J]. Materials Reports, 2018, 32(3): 398 -404 .
[5] Yingke WU,Jianzhong MA,Yan BAO. Advances in Interfacial Interaction Within Polymer Matrix Nanocomposites[J]. Materials Reports, 2018, 32(3): 434 -442 .
[6] Zhengrong FU,Xiuchang WANG,Qinglin JIN,Jun TAN. A Review of the Preparation Techniques for Porous Amorphous Alloys and Their Composites[J]. Materials Reports, 2018, 32(3): 473 -482 .
[7] Fangyuan DONG,Shansuo ZHENG,Mingchen SONG,Yixin ZHANG,Jie ZHENG,Qing QIN. Research Progress of High Performance ConcreteⅡ: Durability and Life Prediction Model[J]. Materials Reports, 2018, 32(3): 496 -502 .
[8] Lixiong GAO,Ruqian DING,Yan YAO,Hui RONG,Hailiang WANG,Lei ZHANG. Microbial-induced Corrosion of Concrete: Mechanism, Influencing Factors,Evaluation Indices, and Proventive Techniques[J]. Materials Reports, 2018, 32(3): 503 -509 .
[9] Ningning HE,Chenxi HOU,Xiaoyan SHU,Dengsheng MA,Xirui LU. Application of SHS Technique for the High-level Radioactive Waste Disposal[J]. Materials Reports, 2018, 32(3): 510 -514 .
[10] Haoran CHEN, Yingdong XIA, Yonghua CHEN, Wei HUANG. Low-dimensional Perovskites: a Novel Candidate Light-harvesting Material for Solar Cells that Combines High Efficiency and Stability[J]. Materials Reports, 2018, 32(1): 1 -11 .
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed