Machine-Learning Informed Representations for Grain Boundary Structures
The atomic structure of grain boundaries plays a defining but poorly understood role in the properties they exhibit. Due to the complex nature of these structures, machine learning is a natural tool for extracting meaningful relationships and new physical insight. We apply a new structural represent...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-07-01
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| Series: | Frontiers in Materials |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fmats.2019.00168/full |