Machine learned synthesizability predictions aided by density functional theory
In data-driven approaches for materials discovery, it is essential to account for phase stability when predicting synthesizability. Here, by combining density functional theory calculations and machine learning, the authors predict the synthesizability of unreported half-Heusler compositions.
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
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| Series: | Communications Materials |
| Online Access: | https://doi.org/10.1038/s43246-022-00295-7 |