Identifying superionic conductors by materials informatics and high-throughput synthesis
High-throughput prediction and synthesis are vital for obtaining new materials that deviate from existing compositions. Here, machine learning is combined with high-throughput synthesis to identify superionic conductors based on Ca-(Nb,Ta)-Bi-O.
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/37c9bea1495d4ca7b93080fed99149bd |
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| Sumario: | High-throughput prediction and synthesis are vital for obtaining new materials that deviate from existing compositions. Here, machine learning is combined with high-throughput synthesis to identify superionic conductors based on Ca-(Nb,Ta)-Bi-O. |
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