Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases
Year: 2016
Type: article
Abstract: Metallic glasses attract considerable interest due to their unique combination of superb properties and processability. Predicting their formation from known alloy parameters remains the major hindrance to the discovery of new systems. Here, we propose a descriptor based on the heuristics that structural and energetic 'confusion' obstructs crystalline growth, and demonstrate its validity by experiments on two well-known glass-forming alloy systems. We then develop a robust model for predicting glass formation ability based on the geometrical and energetic features of crystalline phases calculated ab initio in the AFLOW framework. Our findings indicate that the formation of metallic glass phases could be much more common than currently thought, with more than 17% of binary alloy systems potential glass formers. Our approach pinpoints favourable compositions and demonstrates that smart descriptors, based solely on alloy properties available in online repositories, offer the sought-after key for accelerated discovery of metallic glasses.
Source: Nature Communications
DOI: 10.1038/ncomms12315
Institutions: Duke University, Harvard University, Yale University
Language: English
Subfield: Metals and Alloys
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Awards: G1020486556, G1587441952, G1883336974, DMREF/GOALI/Collaborative Research: High-Throughput Simulations and Experiments to Develop Metallic Glasses, G248758215, DMREF/GOALI/Collaborative Research: High-Throughput Simulations and Experiments to Develop Metallic Glasses, G2972778080, DMREF/GOALI/Collaborative Research: High-Throughput Simulations and Experiments to Develop Metallic Glasses, TOPOLOGICAL DECOMPOSITIONS AND SPECTRAL SAMPLING ALGORITHMS FOR ELEMENT SUBSTITUTION IN CRITICAL TECHNOLOGIES, G3366412161, G3576276198, G3953734904, G4881979844, NNIN: National Nanotechnology Infrastructure Network, Center for Innovative Structures and Phenomena, G6876454421, G7680072868, G7720001552, Materials Research Science and Engineering Center, G8876996369, G8898047084
OpenAlex ID: W2417328420
