Abstract
Bulk metallic glasses (BMGs) are a unique class of materials that are gaining traction in a wide variety of applications due to their attractive physical properties. One limitation to the wide-scale use of these materials is the lack of predictable tools for understanding the relationships between alloy composition and ideal properties. To address this issue, we developed a framework for designing metallic glasses using machine learning (ML) models that predict three key properties of candidate BMG compositions: ability to exist in an amorphous state, critical casting diameter (Dmax), and supercooled liquid range (ΔTx). Our models take only the composition of the alloy as input, and were created from a database of more than 8000 metallic glass experiments assembled from several dozen papers and handbooks. We employed these ML models to optimize the properties of existing commercial alloys and found, experimentally, several of our ML-predicted compositions can form glasses and exceed existing alloys in one of our two design variables, ΔTx.
Original language | English (US) |
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Pages (from-to) | 102-111 |
Number of pages | 10 |
Journal | Acta Materialia |
Volume | 159 |
DOIs | |
State | Published - Oct 15 2018 |
Funding
This work was performed under the following financial assistance award 70NANB14H012 from U.S. Department of Commerce , National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD) .
Keywords
- Bulk metallic glass
- Machine learning
- Materials design
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Ceramics and Composites
- Polymers and Plastics
- Metals and Alloys