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...
Những tác giả chính: | Alejandro Rodriguez, Changpeng Lin, Hongao Yang, Mohammed Al-Fahdi, Chen Shen, Kamal Choudhary, Yong Zhao, Jianjun Hu, Bingyang Cao, Hongbin Zhang, Ming Hu |
---|---|
Định dạng: | Bài viết |
Ngôn ngữ: | English |
Được phát hành: |
Nature Portfolio
2023-02-01
|
Loạt: | npj Computational Materials |
Truy cập trực tuyến: | https://doi.org/10.1038/s41524-023-00974-0 |
Những quyển sách tương tự
-
Unlocking phonon properties of a large and diverse set of cubic crystals by indirect bottom-up machine learning approach
Bằng: Alejandro Rodriguez, et al.
Được phát hành: (2023-08-01) -
Unveiling the phonon scattering mechanisms in half-Heusler thermoelectric compounds
Bằng: He, Ran, et al.
Được phát hành: (2022) -
Effect of Strain on the Electronic Structure and Phonon Stability of SrBaSn Half Heusler Alloy
Bằng: Shakeel Ahmad Khandy, et al.
Được phát hành: (2022-06-01) -
Vacancy-mediated anomalous phononic and electronic transport in defective half-Heusler ZrNiBi
Bằng: Wuyang Ren, et al.
Được phát hành: (2023-08-01) -
Span tables for purlins /
Bằng: 342904 Chu, Yue Pun, et al.
Được phát hành: ([19-)