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
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页数:31
相关论文
共 261 条
  • [1] Efficient exploration of the High Entropy Alloy composition-phase space
    Abu-Odeh, A.
    Galvan, E.
    Kirk, T.
    Mao, H.
    Chen, Q.
    Mason, P.
    Malak, R.
    Arroyave, R.
    [J]. ACTA MATERIALIA, 2018, 152 : 41 - 57
  • [2] Analysis of heterogeneous deformation and dislocation dynamics in single crystal micropillars under compression
    Akarapu, S.
    Zbib, H. M.
    Bahr, D. F.
    [J]. INTERNATIONAL JOURNAL OF PLASTICITY, 2010, 26 (02) : 239 - 257
  • [3] Geometrically projected discrete dislocation dynamics
    Akhondzadeh, Sh
    Sills, R. B.
    Papanikolaou, S.
    Van der Giessen, E.
    Cai, W.
    [J]. MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING, 2018, 26 (06)
  • [4] Application of artificial neural networks in micromechanics for polycrystalline metals
    Ali, Usman
    Muhammad, Waqas
    Brahme, Abhijit
    Skiba, Oxana
    Inal, Kaan
    [J]. INTERNATIONAL JOURNAL OF PLASTICITY, 2019, 120 : 205 - 219
  • [5] Ananthakrishna G, 2007, DISCLOC SOLIDS, V13, P81, DOI 10.1016/S1572-4859(07)80005-2
  • [6] [Anonymous], 2007, Pearson's crystal data: Crystal structure database for inorganic compounds
  • [7] Arceri F., ARXIV200609725
  • [8] Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species
    Artrith, Nongnuch
    Urban, Alexander
    Ceder, Gerbrand
    [J]. PHYSICAL REVIEW B, 2017, 96 (01)
  • [9] High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
    Artrith, Nongnuch
    Morawietz, Tobias
    Behler, Joerg
    [J]. PHYSICAL REVIEW B, 2011, 83 (15):
  • [10] Predicting superhard materials via a machine learning informed evolutionary structure search
    Avery, Patrick
    Wang, Xiaoyu
    Oses, Corey
    Gossett, Eric
    Proserpio, Davide M.
    Toher, Cormac
    Curtarolo, Stefano
    Zurek, Eva
    [J]. NPJ COMPUTATIONAL MATERIALS, 2019, 5 (1)