Cited by 139Metallic Glasses and Amorphous AlloysMachine Learning in Materials ScienceMaterial Dynamics and Properties

Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases

Open access
Perim, Eric, Lee, Dongwoo, Liu, Yanhui, Toher, Cormac, Gong, Pan, Li, Yanglin, Simmons, W. Neal, Levy, Ohad +3 more·2016·Nature Communications

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.

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  • Perim et al. identified an energy spectral descriptor for de novo prediction of metallic glasses and used it to quantify the classification probability of mixtures.

    “For example, Perim et al.201 identified an energy spectral descriptor for de novo prediction of metallic glasses and used it to quantify the classification probability of mixtures.”

    From QSAR without borders (2020) · cites this paper as [201]

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Authors
Perim, E., Lee, D., Liu, Y., Toher, C., Gong, P., Li, Y., Simmons, W. N., Levy, O., Vlassak, J. J., Schroers, J., & Curtarolo, S.
Affiliations
  • Duke University (US)
  • Harvard University (US)
  • Yale University (US)
Venue
Nature Communications
Published
2016-08-02
Volume
7
Issue
1
Pages
12315-12315
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2417328420
Cited by
139
References
86
Citations by year
2026: 6 · 2025: 16 · 2024: 13 · 2023: 9 · 2022: 8 · 2021: 13 · 2020: 13 · 2019: 23 · 2018: 22 · 2017: 14 · 2016: 2
FWCI
18.08
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1436151, 1436268, 1435820, ECS- 03-35765, 0335765, 1119826, 1420570)
  • U.S. Department of Defense (N000141310635, ECS-0335765)
  • Yale University
  • Harvard University (ECS-0335765, DMR-1420570)
  • Office of Naval Research (N00014-13- 1-0635, N00014-14-1-0526, N00014, ECS-0335765)
  • Division of Materials Research (DMR 14–20570, DMR-1435820, 1420570, DMR-1119826, DMR-1436151, 1119826)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Metallic Glasses and Amorphous Alloys
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Materials Science › Materials Chemistry › Material Dynamics and Properties
Keywords
bulk metallic glasses, glass-forming ability, spectral descriptors, crystallization thermodynamics, binary alloys, AFLOW, first-principles calculations, machine learning descriptors, alloy composition prediction

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