ResourceType

Dataset

DOI

https://doi.org/10.7936/6rxs-dq72

Grant/Award Number and Agency

NSF Grant #DMR-2004630

funderName

National Science Foundation

awardNumber

2004630

awardURI

https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004630&HistoricalAwards=false

Abstract

Despite intense interest in the discovery and design of metallic glasses, the efficient a priori identification of novel glass-formers without the need for time-consuming experimental characterization has remained an unattained goal. To address this, we use geometric alignment and density-based clustering algorithms to quantitatively describe the short-range atomic structure in the simulated liquid state for five known metallic glass-forming systems. We show that each liquid is comprised of a surprisingly small number of geometrically-similar atomic clusters (6–8 characteristic motifs in the systems studied) and that the variance of the population distribution of these clusters in the high temperature liquid is inversely correlated to the experimentally-observed glass-forming ability (GFA) as a function of composition within each system studied. These correlations are observed without consideration of temperature-dependent evolution or longer range atomic arrangements, which are much more time-consuming to evaluate. The relative simplicity and broad applicability of this technique to both good glass-forming systems (Cu–Zr, Ni–Nb, Al–Ni–Zr) and poor glass-forming systems (Al–Sm, Au–Si) suggests that the population of characteristic atomic clusters in the simulated liquid could be used as an efficient, high-throughput screening method for identification of potential glass-forming alloys.

ORCID

Porter Weeks https://orcid.org/000-0001-5617-6273

Rights

http://creativecommons.org/licenses/by/4.0/

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Share

COinS
 

Publication Date

2022