Robust combined modeling of crystalline and amorphous silicon grain boundary conductance by machine learning
Abstract Knowledge in thermal and electric transport through grain boundary (GB) is crucial for designing nanostructured thermoelectric materials, where the transport greatly depends on GB atomistic structure. In this work, we employ machine learning (ML) techniques to study the relationship between...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-022-00898-1 |