Loading…

Review on Quantum Mechanically Guided Design of Ultra-Strong Metallic Glasses

Quantum mechanically guided materials design has been used to predict the mechanical property trends in crystalline materials. Thereby, the identification of composition-structure-property relationships is enabled. However, quantum mechanics based design guidelines and material selection criteria fo...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in materials 2020-04, Vol.7
Main Authors: Evertz, Simon, Schnabel, Volker, Köhler, Mathias, Kirchlechner, Ines, Kontis, Paraskevas, Chen, Yen-Ting, Soler, Rafael, Jaya, B. Nagamani, Kirchlechner, Christoph, Music, Denis, Gault, Baptiste, Schneider, Jochen M., Raabe, Dierk, Dehm, Gerhard
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Quantum mechanically guided materials design has been used to predict the mechanical property trends in crystalline materials. Thereby, the identification of composition-structure-property relationships is enabled. However, quantum mechanics based design guidelines and material selection criteria for ultra-strong metallic glasses have been lacking. Hence, based on an ab initio model for metallic glasses in conjunction with an experimental high-throughput methodology geared toward revealing the relationship between chemistry, topology and mechanical properties, we propose principles for the design of tough as well as stiff metallic glasses. The main design notion is that a low fraction of hybridized bonds compared to the overall bonding in a metallic glass can be used as a criterion for the identification of damage-tolerant metallic glass systems. To enhance the stiffness of metallic glasses, the bond energy density must be increased as the bond energy density is the origin of stiffness in metallic glasses. The thermal expansion, which is an important glass-forming identifier, can be predicted based on the Debye-Grüneisen model.
ISSN:2296-8016
2296-8016
DOI:10.3389/fmats.2020.00089