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...
| Päätekijät: | , , , , , , , , , , |
|---|---|
| Aineistotyyppi: | Artikkeli |
| Kieli: | English |
| Julkaistu: |
Nature Portfolio
2023-02-01
|
| Sarja: | npj Computational Materials |
| Linkit: | https://doi.org/10.1038/s41524-023-00974-0 |