Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding
Abstract Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic scale. An...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-09-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-021-00613-6 |