Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

被引:31
作者
Frydrych, Karol [1 ]
Karimi, Kamran [1 ]
Pecelerowicz, Michal [1 ]
Alvarez, Rene [1 ]
Dominguez-Gutierrez, Francesco Javier [1 ,2 ]
Rovaris, Fabrizio [1 ]
Papanikolaou, Stefanos [1 ]
机构
[1] Natl Ctr Nucl Res, NOMATEN Ctr Excellence, Ul A Soltana 7, PL-05400 Otwock, Poland
[2] SUNY Stony Brook, Inst Adv Computat Sci, Stony Brook, NY 11749 USA
基金
欧盟地平线“2020”;
关键词
metal alloys; machine learning; informatics; defects; dislocations; mechanical deformation; data science; ontology; ARTIFICIAL NEURAL-NETWORK; DISCRETE DISLOCATION DYNAMICS; HIGH-ENTROPY ALLOYS; MACHINE LEARNING PREDICTION; GLASS-FORMING ABILITY; HIGH-CYCLE FATIGUE; CRYSTAL PLASTICITY; DATA SCIENCE; PROCESSING PARAMETERS; ELASTIC PROPERTIES;
D O I
10.3390/ma14195764
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials' discovery or develop new understanding of materials' behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.
引用
收藏
页数:31
相关论文
共 261 条
  • [91] Hume-Rothery W, 1935, Z KRISTALLOGR, V91, P23
  • [92] Hume-Rothery W., 1954, The Structure of Metals and Alloys
  • [93] Stabilization of metallic supercooled liquid and bulk amorphous alloys
    Inoue, A
    [J]. ACTA MATERIALIA, 2000, 48 (01) : 279 - 306
  • [94] Isayev O., 2019, Materials informatics: methods, tools, and applications
  • [95] Machine learning for phase selection in multi-principal element alloys
    Islam, Nusrat
    Huang, Wenjiang
    Zhuang, Houlong L.
    [J]. COMPUTATIONAL MATERIALS SCIENCE, 2018, 150 : 230 - 235
  • [96] Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys
    Jafary-Zadeh, Mehdi
    Khoo, Khoong Hong
    Laskowski, Robert
    Branicio, Paulo S.
    Shapeev, Alexander, V
    [J]. JOURNAL OF ALLOYS AND COMPOUNDS, 2019, 803 : 1054 - 1062
  • [97] Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
    Jain, Anubhav
    Shyue Ping Ong
    Hautier, Geoffroy
    Chen, Wei
    Richards, William Davidson
    Dacek, Stephen
    Cholia, Shreyas
    Gunter, Dan
    Skinner, David
    Ceder, Gerbrand
    Persson, Kristin A.
    [J]. APL MATERIALS, 2013, 1 (01):
  • [98] Enabling deeper learning on big data for materials informatics applications
    Jha, Dipendra
    Gupta, Vishu
    Ward, Logan
    Yang, Zijiang
    Wolverton, Christopher
    Foster, Ian
    Liao, Wei-keng
    Choudhary, Alok
    Agrawal, Ankit
    [J]. SCIENTIFIC REPORTS, 2021, 11 (01)
  • [99] A comparative study on Arrhenius-type constitutive model and artificial neural network model to predict high-temperature deformation behaviour in Aermet100 steel
    Ji, Guoliang
    Li, Fuguo
    Li, Qinghua
    Li, Huiqu
    Li, Zhi
    [J]. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2011, 528 (13-14): : 4774 - 4782
  • [100] Jones M., 2017, CODEMETA EXCHANGE SC