Deep neural networks for accurate predictions of crystal stability
Crystal stability prediction is of paramount importance for novel material discovery, with theoretical approaches alternative to expensive standard schemes highly desired. Here the authors develop a deep learning approach which, just using two descriptors, provides crystalline formation energies wit...
Guardado en:
Autores principales: | Weike Ye, Chi Chen, Zhenbin Wang, Iek-Heng Chu, Shyue Ping Ong |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2018
|
Materias: | |
Acceso en línea: | https://doaj.org/article/4d18a79643814bec9e968a7650b9f51d |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Deep convolutional neural networks for accurate somatic mutation detection
por: Sayed Mohammad Ebrahim Sahraeian, et al.
Publicado: (2019) -
Deep generative neural network for accurate drug response imputation
por: Peilin Jia, et al.
Publicado: (2021) -
Detection of eye contact with deep neural networks is as accurate as human experts
por: Eunji Chong, et al.
Publicado: (2020) -
Accurate deep neural network inference using computational phase-change memory
por: Vinay Joshi, et al.
Publicado: (2020) -
Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network
por: R. Raksasat, et al.
Publicado: (2021)