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

被引：33

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

来源：

MATERIALS | 2021年 / 14卷 / 19期

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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