Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases
Open accessAbstract
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.”
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Interpretable models for extrapolation in scientific machine learning
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AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis
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A general and transferable deep learning framework for predicting phase formation in materials
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Surface-engineered nanostructured high-entropy alloys for advanced electrocatalysis
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Crystallization behavior upon heating and cooling in Cu50Zr50 metallic glass thin films
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How Many Bulk Metallic Glasses Are There?
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AFLUX: The LUX materials search API for the AFLOW data repositories
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A Review of the Preparation, Machining Performance, and Application of Fe-Based Amorphous Alloys
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A High-Throughput Structural and Electrochemical Study of Metallic Glass Formation in Ni–Ti–Al
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High-throughput crystal structure solution using prototypes
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aflow++: A C++ framework for autonomous materials design
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Self-citationMetallic glasses for biodegradable implants
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Self-citationThe glassy solid as a statistical ensemble of crystalline microstates
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ReactionDataExtractor: A Tool for Automated Extraction of Information from Chemical Reaction Schemes
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Defect Genome of Cubic Perovskites for Fuel Cell Applications
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Percolated Strain Networks and Universal Scaling Properties of Strain Glasses
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Are hints about glass forming ability hidden in the liquid structure?
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The effect of Y addition on the crystallization behaviors of Zr-Cu-Ni-Al bulk metallic glasses
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A perspective on metallic liquids and glasses
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The AFLOW library of crystallographic prototypes: Part 4
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The AFLOW Fleet for Materials Discovery
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Progress in the preparation, forming and machining of metallic glasses
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Exceptionally broad bulk metallic glass formation in the Mg–Cu–Yb system
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Self-citationShort-range ordering in metallic supercooled liquids and glasses
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Cu-Zr-Ti-Al metallic glass: Thermodynamic prediction, synthesis, and biocorrosion studies
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Ab initio design of Zr-based bulk metallic glass for high-strength and optical coating applications
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Self-citationLatest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses
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Glass formation in binary alloys with different atomic symmetries
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Simulating high-entropy alloys at finite temperatures: An uncertainty-based approach
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Self-citationThermodynamic modelling to optimize glass forming composition in multicomponent Zr-Cu-Co-Al system
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Comprehensive characterization of the structure of Zr-based metallic glasses
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High-temperature Mo-based bulk metallic glass with enhanced glass forming ability
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Competing Hexagonal and Square Lattices on a Spherical Surface
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Revealing Nanoindentation Size-Dependent Creep Behavior in a La-Based Metallic Glassy Film
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The AFLOW Fleet for Materials Discovery
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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)
- ORCID
- Venue
- Nature Communications
- Published
- 2016-08-02
- Volume
- 7
- Issue
- 1
- Pages
- 12315-12315
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC4974662
- 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
- gold
- doi.org
- 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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