Cargando…
Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery
The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and the other that only uses stoichiometric informati...
Autores principales: | Ihalage, Achintha, Hao, Yang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9218748/ https://www.ncbi.nlm.nih.gov/pubmed/35475548 http://dx.doi.org/10.1002/advs.202200164 |
Ejemplares similares
-
Deep learning framework for subject-independent emotion detection using wireless signals
por: Khan, Ahsan Noor, et al.
Publicado: (2021) -
Active Learning Optimisation of Binary Coded Metasurface Consisting of Wideband Meta-Atoms
por: Chittur Subramanianprasad, Parvathy, et al.
Publicado: (2023) -
Crystal graph attention networks for the prediction of stable materials
por: Schmidt, Jonathan, et al.
Publicado: (2021) -
Network graph representation of COVID-19 scientific publications to aid knowledge discovery
por: Cernile, George, et al.
Publicado: (2021) -
Materials fatigue prediction using graph neural networks on microstructure representations
por: Thomas, Akhil, et al.
Publicado: (2023)