Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 7 September 2021 / Approved: 9 September 2021 / Online: 9 September 2021 (11:22:52 CEST)

How to cite: 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. Preprints 2021, 2021090176 (doi: 10.20944/preprints202109.0176.v1). 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. Preprints 2021, 2021090176 (doi: 10.20944/preprints202109.0176.v1).

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

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