Frydrych, K.; Karimi, K.; Pecelerowicz, M.; Alvarez, R.; Dominguez-Gutiérrez, F.J.; Rovaris, F.; Papanikolaou, S. Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges. Materials2021, 14, 5764.
Frydrych, K.; Karimi, K.; Pecelerowicz, M.; Alvarez, R.; Dominguez-Gutiérrez, F.J.; Rovaris, F.; Papanikolaou, S. Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges. Materials 2021, 14, 5764.
Frydrych, K.; Karimi, K.; Pecelerowicz, M.; Alvarez, R.; Dominguez-Gutiérrez, F.J.; Rovaris, F.; Papanikolaou, S. Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges. Materials2021, 14, 5764.
Frydrych, K.; Karimi, K.; Pecelerowicz, M.; Alvarez, R.; Dominguez-Gutiérrez, F.J.; Rovaris, F.; Papanikolaou, S. Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges. Materials 2021, 14, 5764.
Abstract
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.
Keywords
Metal alloys; Machine learning; Informatics; Defects; Dislocations; Mechanical deformation; Data science; Ontology
Subject
Chemistry and Materials Science, Metals, Alloys and Metallurgy
Copyright:
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