Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table
Abstract Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottleneck with the developed Elemental Sp...
| 主要な著者: | , , , , , , , , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Nature Portfolio
2023-02-01
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| シリーズ: | npj Computational Materials |
| オンライン･アクセス: | https://doi.org/10.1038/s41524-023-00974-0 |