Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
Machine-learning approaches based on DFT computations can greatly enhance materials discovery. Here the authors leverage existing large DFT-computational data sets and experimental observations by deep transfer learning to predict the formation energy of materials from their elemental compositions w...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2019-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-13297-w |